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Data Science / AI Manager

Efds Fa Em5 Oraclecloud Com CX 1 · GTBC Office Building 02, Chennai, TN, IN · Hybrid · Deleted · Oracle Recruiting Cloud / Fusion HCM

Job facts

Field	Value
Company	Efds Fa Em5 Oraclecloud Com CX 1
Title	Data Science / AI Manager
Normalized title	-
Department / team	Enterprise Technology
Location	Chennai, TN, United States
Work model	Hybrid / Hybrid
Employment type	Full Time
Salary	-
Status	deleted
ATS provider	Oracle Recruiting Cloud / Fusion HCM
Posted / first seen	2026-06-19 / 2026-06-20
Changed / last seen	2026-06-23 / 2026-06-21

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Page	What it contains	Open
Company jobs	Active postings from Efds Fa Em5 Oraclecloud Com CX 1.	Open
Company breakdowns	Role, location, ATS, and work model facets for this company.	Open
ATS provider jobs	Active postings observed through Oracle Recruiting Cloud / Fusion HCM.	Open
Provider filtered search	The same provider as a filtered job collection.	Open
City jobs	Active postings in Chennai.	Open
Department jobs	Active postings in Enterprise Technology.	Open
Work model jobs	Active Hybrid postings.	Open
Lifecycle events	Open, update, close, and reopen events for this posting.	Open
Original posting	Canonical source or apply URL captured from the ATS.	Open

Linked records

Company	Efds Fa Em5 Oraclecloud Com CX 1
Source	d8110a61-5510-417b-a74c-f58816339c6b
ATS provider	Oracle Recruiting Cloud / Fusion HCM

Description

Description GDI&A - Data Science, Machine Learning and AI Product Manager Global Data Insights and Analytics ( GDI&A ) navigates Ford Motor Company through the disruptions of the information age, unleashing the power of Data, Advanced Analytics, and AI to drive value and help transform Ford Motor Company through proactive, actionable, evidence-based decision making. GDI&A delivers data-driven platforms, advanced analytics, and AI/ML solutions to support Ford’s Industrial System, Sales, Service, Marketing, Strategy, Finance, and Ford Credit. We are seeking an initiative-taking and experienced candidate to lead and mentor a multi-skilled product team comprising of data scientists, machine learning engineers, product designers, and software engineers through the entire lifecycle of complex analytical and AI/ML data science products. This role demands strong technical expertise, hands-on experience with cutting-edge analytical tools (including AI/ML), strong understanding of software craftmanship & software development cycle, thought leadership and ability to translate business needs into analytical formulations, and a proven track record of success in creating impactful and insightful data science products. Ideal candidate for this role possesses a strong theoretical foundation in statistical analysis, machine learning, simulation, econometrics, optimization, data platforms, AI, and software design & development. Given the dynamic nature of data analytics and AI, a commitment to continuous learning, upskilling, and being hands-on is essential. This role requires a blend of technical expertise, hands-on experience, strategic thinking, business domain knowledge, thought leadership, and strong leadership skills. Domain knowledge in Ford processes, Product Development, Program and Launch management is an added advantage. Key Responsibilities: Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required. Team Leadership & Mentorship : Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy. Innovation : Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development. Domain Expertise : Lead engagement with internal business partners, understand the business domain (e.g., product development, marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations. Experience in Product development domain adds a distinct advantage. Communication : Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners. GenAI Implementation : Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows. Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives. Qualifications: Required: Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred). 5+ years of leading a team of cross-functional technical experts. Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost). Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive). Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies. Strong technical & people leadership, communication, and collaboration skills. Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions. Preferred: Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques. Familiarity with computationally intensive statistical methods, Jira, system architecture principles. Google certifications on developing and deploying AI/ML models on GCP. Responsibilities Key Responsibilities: Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required. Team Leadership & Mentorship : Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy. Innovation : Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development. Domain Expertise : Lead engagement with internal business partners, understand the business domain (e.g., marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations. Communication : Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners. GenAI Implementation : Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows. Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives. Qualifications Required: Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred). 5+ years of leading a team of cross-functional technical experts. Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost). Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive). Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies. Strong technical & people leadership, communication, and collaboration skills. Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions. Preferred: Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques. Familiarity with computationally intensive statistical methods, Jira, system architecture principles. Google certifications on developing and deploying AI/ML models on GCP.

Full job record

Job ID	13aa953ff6d6360177615cac562a560a0355880a
Org ID	fc791186-3bfa-4e9d-8648-0d2f6f66937d
Source ID	d8110a61-5510-417b-a74c-f58816339c6b
Board ID	d8110a61-5510-417b-a74c-f58816339c6b
Provider	oracle_hcm
Provider Job Key	65412
Title	Data Science / AI Manager
Normalized Title	—
Status	deleted
Active	no
Location Text	GTBC Office Building 02, Chennai, TN, IN
Department	Enterprise Technology
Team	—
Employment Type	full_time
Workplace Type	hybrid
Remote Policy	hybrid
Country	United States
Region	TN
City	Chennai
Salary Raw	Description GDI&A - Data Science, Machine Learning and AI Product Manager Global Data Insights and Analytics ( GDI&A ) navigates Ford Motor Company through the disruptions of the information age, unleashing the power of Data, Advanced Analytics, and AI to drive value and help transform Ford Motor Company through proactive, actionable, evidence-based decision making. GDI&A delivers data-driven platforms, advanced analytics, and AI/ML solutions to support Ford’s Industrial System, Sales, Service, Marketing, Strategy, Finance, and Ford Credit. We are seeking an initiative-taking and experienced candidate to lead and mentor a multi-skilled product team comprising of data scientists, machine learning engineers, product designers, and software engineers through the entire lifecycle of complex analytical and AI/ML data science products. This role demands strong technical expertise, hands-on experience with cutting-edge analytical tools (including AI/ML), strong understanding of software craftmanship & software development cycle, thought leadership and ability to translate business needs into analytical formulations, and a proven track record of success in creating impactful and insightful data science products. Ideal candidate for this role possesses a strong theoretical foundation in statistical analysis, machine learning, simulation, econometrics, optimization, data platforms, AI, and software design & development. Given the dynamic nature of data analytics and AI, a commitment to continuous learning, upskilling, and being hands-on is essential. This role requires a blend of technical expertise, hands-on experience, strategic thinking, business domain knowledge, thought leadership, and strong leadership skills. Domain knowledge in Ford processes, Product Development, Program and Launch management is an added advantage. Key Responsibilities: Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required. Team Leadership & Mentorship : Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy. Innovation : Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development. Domain Expertise : Lead engagement with internal business partners, understand the business domain (e.g., product development, marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations. Experience in Product development domain adds a distinct advantage. Communication : Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners. GenAI Implementation : Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows. Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives. Qualifications: Required: Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred). 5+ years of leading a team of cross-functional technical experts. Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost). Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive). Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies. Strong technical & people leadership, communication, and collaboration skills. Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions. Preferred: Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques. Familiarity with computationally intensive statistical methods, Jira, system architecture principles. Google certifications on developing and deploying AI/ML models on GCP. Responsibilities Key Responsibilities: Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required. Team Leadership & Mentorship : Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy. Innovation : Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development. Domain Expertise : Lead engagement with internal business partners, understand the business domain (e.g., marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations. Communication : Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners. GenAI Implementation : Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows. Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives. Qualifications Required: Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred). 5+ years of leading a team of cross-functional technical experts. Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost). Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive). Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies. Strong technical & people leadership, communication, and collaboration skills. Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions. Preferred: Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques. Familiarity with computationally intensive statistical methods, Jira, system architecture principles. Google certifications on developing and deploying AI/ML models on GCP.
Salary Min	—
Salary Max	—
Salary Currency	—
Salary Period	—
Source URL	https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65412
Apply URL	https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65412
First Seen At	2026-06-20 12:39:00Z
Last Seen At	2026-06-21 13:00:30Z
Last Checked At	2026-06-23 11:40:16Z
Last Changed At	2026-06-23 11:40:16Z
Inactive At	2026-06-23 11:40:16Z
Source Posted At	2026-06-19 18:04:11Z
Source Updated At	—
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    "ExternalDescriptionStr": "<p><span><strong>GDI&amp;A - Data Science, Machine Learning and AI&nbsp;</strong></span></p><p><span><strong>Product Manager&nbsp;</strong></span></p><p class=\"paragraph\" style=\"margin: 0in;\">&nbsp;</p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\"><span>Global Data Insights and Analytics (<strong>GDI&amp;A</strong>) navigates Ford Motor Company through the disruptions of the information age, unleashing the power of Data, Advanced Analytics, and AI to drive&nbsp;value and help transform Ford Motor Company through proactive, actionable, evidence-based decision making. GDI&amp;A delivers data-driven platforms, advanced analytics, and AI/ML solutions to support Ford’s Industrial System, Sales, Service, Marketing, Strategy, Finance, and Ford Credit.</span></p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\">&nbsp;</p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\"><span>We are seeking an initiative-taking and experienced candidate to lead and mentor a multi-skilled product team comprising of data scientists, machine learning engineers, product designers, and software engineers through the entire lifecycle of complex analytical and AI/ML data science products.&nbsp;This role demands strong technical expertise, hands-on experience with cutting-edge analytical tools (including AI/ML), strong understanding of software craftmanship &amp; software development cycle, thought leadership and ability to translate business needs into analytical formulations, and a proven track record of success in creating impactful and insightful data science products.&nbsp;</span></p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\">&nbsp;</p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\"><span>Ideal candidate for this role possesses a strong theoretical foundation in statistical analysis, machine learning, simulation, econometrics, optimization, data platforms, AI, and software design &amp; development. Given the dynamic nature of data analytics and AI, a commitment to continuous learning, upskilling, and being hands-on is essential. This role requires a blend of technical expertise, hands-on experience, strategic thinking, business domain knowledge, thought leadership, and strong leadership skills. Domain knowledge in Ford processes, Product Development, Program and Launch management is an added advantage.</span></p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 12.15pt;\">&nbsp;</p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\"><span><strong>Key Responsibilities:</strong></span></p><p class=\"paragraph\" style=\"margin: 0in;\">&nbsp;</p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Technical Leadership:</strong>&nbsp;Provide expert technical guidance and domain mentorship to data scientists, data engineers &amp; software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices.&nbsp;Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Team Leadership &amp; Mentorship</strong>:&nbsp;Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science.&nbsp;Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Innovation</strong>: Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Domain Expertise</strong>: Lead engagement with internal business partners, understand the business domain (e.g., product development, marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data &amp; analytical problem formulations. </span><span data-teams=\"true\">Experience in Product development domain adds a distinct advantage.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Communication</strong>: Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate&nbsp;compelling data-driven recommendations to senior management and foster strong relationships with key business partners.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>GenAI Implementation</strong>: Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows.</span></p></li><li><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in;\"><span><strong>Cross-functional Collaboration:</strong>&nbsp;Work effectively with various teams and stakeholders to achieve business objectives.</span></p></li></ul><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\">&nbsp;</p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\"><span><strong>Qualifications:</strong></span></p><p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt;\">&nbsp;</p><p><span><strong>Required:</strong></span></p><ul style=\"list-style-type: disc;\"><li><span>Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI&nbsp;</span></li><li><span>5+ years of </span><i><span><strong>hands-on experience</strong></span></i><span> applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred).</span></li><li><span>5+ years of leading a team of cross-functional technical experts.</span></li><li><span>Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost).</span></li><li><span>Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive).</span></li><li><span>Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. 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    "InternalQualificationsStr": "<p style=\"text-indent: 9pt;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\"><strong>Required:</strong></span></span></p><ul style=\"list-style-type: disc;\"><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI&nbsp;</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">5+ years of </span><i><span style=\"font-size: 12pt;\"><strong>hands-on experience</strong></span></i><span style=\"font-size: 12pt;\"> applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred).</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">5+ years of leading a team of cross-functional technical experts.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost).</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive).</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Strong technical &amp; people leadership, communication, and collaboration skills.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions.</span></span></p></li></ul><p style=\"text-align: justify;\">&nbsp;</p><p style=\"text-indent: 9pt;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\"><strong>Preferred:</strong></span></span></p><ul style=\"list-style-type: disc;\"><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Familiarity with computationally intensive statistical methods, Jira, system architecture principles.</span></span></p></li><li class=\"ortl-align-justify\"><p style=\"text-align: justify;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><span style=\"font-size: 12pt;\">Google certifications on developing and deploying AI/ML models on GCP.</span></span></p></li></ul>",
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    "ExternalResponsibilitiesStr": "<p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt; vertical-align: baseline;\"><span style=\"font-family: Arial, sans-serif;\"><span style=\"font-size: 14pt;\"><strong>Key Responsibilities:</strong></span></span></p><p class=\"paragraph\" style=\"margin: 0in; text-align: justify; vertical-align: baseline;\">&nbsp;</p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Technical Leadership:</strong>&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">Provide expert technical guidance and domain mentorship to data scientists, data engineers &amp; software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices.</span><span style=\"font-family: Calibri, sans-serif;\"><span style=\"font-size: 11pt;\">&nbsp;</span></span><span style=\"font-family: Arial, sans-serif;\">Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Team Leadership &amp; Mentorship</strong>:&nbsp;Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science.&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Innovation</strong>: Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Domain Expertise</strong>: Lead engagement with internal business partners, understand the business domain (e.g., marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data &amp; analytical problem formulations.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Communication</strong>: Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">compelling data-driven recommendations to senior management and foster strong relationships with key business partners.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"font-family: Arial, sans-serif;\"><strong>GenAI Implementation</strong>: Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Cross-functional Collaboration:</strong>&nbsp;Work effectively with various teams and stakeholders to achieve business objectives.</span></p></li></ul>",
    "InternalResponsibilitiesStr": "<p class=\"paragraph\" style=\"margin: 0in 0in 0in 9pt; vertical-align: baseline;\"><span style=\"font-family: Arial, sans-serif;\"><span style=\"font-size: 14pt;\"><strong>Key Responsibilities:</strong></span></span></p><p class=\"paragraph\" style=\"margin: 0in; text-align: justify; vertical-align: baseline;\">&nbsp;</p><ul style=\"list-style-type: disc; padding-left: 48px;\"><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Technical Leadership:</strong>&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">Provide expert technical guidance and domain mentorship to data scientists, data engineers &amp; software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices.</span><span style=\"font-family: Calibri, sans-serif;\"><span style=\"font-size: 11pt;\">&nbsp;</span></span><span style=\"font-family: Arial, sans-serif;\">Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Team Leadership &amp; Mentorship</strong>:&nbsp;Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science.&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Innovation</strong>: Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Domain Expertise</strong>: Lead engagement with internal business partners, understand the business domain (e.g., marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data &amp; analytical problem formulations.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Communication</strong>: Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate&nbsp;</span><span style=\"font-family: Arial, sans-serif;\">compelling data-driven recommendations to senior management and foster strong relationships with key business partners.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"font-family: Arial, sans-serif;\"><strong>GenAI Implementation</strong>: Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows.</span></p></li><li class=\"ortl-align-justify\"><p class=\"paragraph\" style=\"margin-bottom: 0in; margin-right: 0in; margin-top: 0in; text-align: justify; vertical-align: baseline;\"><span style=\"color: rgb(17, 24, 39); font-family: Arial, sans-serif;\"><strong>Cross-functional Collaboration:</strong>&nbsp;Work effectively with various teams and stakeholders to achieve business objectives.</span></p></li></ul>",
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