Home › Companies › Fa Ewjt Saasfaprod1 Fa Ocs Oraclecloud Com Cx 2 › Data Scientist - Reinforcement Learning
Data Scientist - Reinforcement Learning
Fa Ewjt Saasfaprod1 Fa Ocs Oraclecloud Com Cx 2 · United States; US New Jersey (JCO) C79 · Hybrid · Active · Oracle Recruiting Cloud / Fusion HCM
Job facts
| Field | Value |
|---|---|
| Company | Fa Ewjt Saasfaprod1 Fa Ocs Oraclecloud Com Cx 2 |
| Title | Data Scientist - Reinforcement Learning |
| Normalized title | - |
| Department / team | Digital |
| Location | United States |
| Work model | Hybrid / Hybrid |
| Employment type | Full Time |
| Salary | - |
| Status | active |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
| Posted / first seen | 2026-06-10 / 2026-06-11 |
| Changed / last seen | 2026-06-20 / 2026-06-21 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Fa Ewjt Saasfaprod1 Fa Ocs Oraclecloud Com Cx 2. | 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 |
| Department jobs | Active postings in Digital. | 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 | Fa Ewjt Saasfaprod1 Fa Ocs Oraclecloud Com Cx 2 |
| Source | 907773df-d032-42dc-b60a-978734f5ac21 |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
Description
Description
Key Responsibilities
• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:
o Q-Learning
o Deep Q Networks (DQN)
o Policy Gradient Methods
o Contextual Bandits
o Markov Decision Processes (MDP)
• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.
Responsibilities
Preferred / Good-to-Have Skill
• Experience in collections, credit risk, customer analytics, or financial services domains.
• Familiarity with:
o Deep Learning frameworks (TensorFlow, PyTorch)
o MLOps and CI/CD workflows
o Real-time decision systems
o Cloud platforms such as AWS, Azure, or GCP
Qualifications
Must-Have Qualifications
• Strong experience in Reinforcement Learning and sequential decision-making systems.
• Hands-on expertise with:
o Reinforcement Learning algorithms (Q-Learning, DQN, PPO, Bandits, etc.)
o Markov Decision Processes (MDP)
Full job record
| Job ID | 64d15ae6bcd5648084346005a3682535473da117 |
| Org ID | 3ea3b397-9a23-408a-8421-50fd1d902746 |
| Source ID | 907773df-d032-42dc-b60a-978734f5ac21 |
| Board ID | 907773df-d032-42dc-b60a-978734f5ac21 |
| Provider | oracle_hcm |
| Provider Job Key | 15094 |
| Title | Data Scientist - Reinforcement Learning |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | United States; US New Jersey (JCO) C79 |
| Department | Digital |
| Team | — |
| Employment Type | full_time |
| Workplace Type | hybrid |
| Remote Policy | hybrid |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | Description Key Responsibilities • Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes. • Build adaptive decisioning systems using techniques such as: o Q-Learning o Deep Q Networks (DQN) o Policy Gradient Methods o Contextual Bandits o Markov Decision Processes (MDP) • Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization. • Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty. • Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions. • Build and maintain machine learning pipelines in Databricks or similar distributed computing environments. • Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment. Responsibilities Preferred / Good-to-Have Skill • Experience in collections, credit risk, customer analytics, or financial services domains. • Familiarity with: o Deep Learning frameworks (TensorFlow, PyTorch) o MLOps and CI/CD workflows o Real-time decision systems o Cloud platforms such as AWS, Azure, or GCP Qualifications Must-Have Qualifications • Strong experience in Reinforcement Learning and sequential decision-making systems. • Hands-on expertise with: o Reinforcement Learning algorithms (Q-Learning, DQN, PPO, Bandits, etc.) o Markov Decision Processes (MDP) |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/cx_2/job/15094 |
| Apply URL | https://fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/cx_2/job/15094 |
| First Seen At | 2026-06-11 11:32:59Z |
| Last Seen At | 2026-06-21 12:52:50Z |
| Last Checked At | 2026-06-21 12:52:50Z |
| Last Changed At | 2026-06-20 12:14:05Z |
| Inactive At | — |
| Source Posted At | 2026-06-10 17:20:37Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=oracle_hcm/board=fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com|cx_2/date=2026-06-21/2026-06-21T12-51-11-391Z-8cdf663e88f0150f2ed55f0616a2982d391879ea3b5899da09ec5f4af68ff69d.json |
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