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Senior Strategy and Analytics Lead
MyPlanAdvocate · Remote (United States), United States · Remote · Deleted · Rippling ATS
Job facts
| Field | Value |
|---|---|
| Company | MyPlanAdvocate |
| Title | Senior Strategy and Analytics Lead |
| Normalized title | - |
| Department / team | Growth |
| Location | United States |
| Work model | Remote / Remote |
| Employment type | Full Time |
| Salary | - |
| Status | deleted |
| ATS provider | Rippling ATS |
| Posted / first seen | 2026-05-06 / 2026-05-29 |
| Changed / last seen | 2026-06-06 / 2026-06-03 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from MyPlanAdvocate. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Rippling ATS. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| Department jobs | Active postings in Growth. | Open |
| Work model jobs | Active Remote 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 | MyPlanAdvocate |
| Source | 79ecfadc-5542-46be-a8fa-492a1b77ca48 |
| ATS provider | Rippling ATS |
Description
company
Join Our Mission
Healthy Labs and MyPlanAdvocate are on a mission to transform how consumers connect with insurance and healthcare solutions. We serve often-overlooked populations and believe that better data, better systems, and better decision-making can materially improve lives.
Healthy Labs operates as our high-scale proving ground for Medicare and insurance distribution. MyPlanAdvocate is the AI-native platform powering our long-term vision: an end-to-end growth, compliance, and operations engine for agencies across insurance verticals.
As part of the MPA portfolio, you will work at the intersection of growth, analytics, AI, and regulated marketplaces — helping shape how modern insurance distribution is built.
role
Senior Strategy and Analytics Lead is needed to perform the following duties:
Funnel Analysis across Marketing, Sales, Operations, Product, and Compliance Systems
Analyzing conversion across MPA's Medicare beneficiary acquisition funnel — from initial lead capture through licensed-agent telephonic intake, Needs Assessment, plan recommendation, and enrollment submission to the carrier. Investigating drop-off points across paid acquisition, partner channels, and direct brand traffic to identify which sources produce leads with the highest end-to-end enrollment conversion. Partnering with the licensed-agent operations team to analyze call-center metrics — speed-to-lead, talk time, completion rate, plan-match accuracy — and connecting those operational metrics back to conversion. Examining post-enrollment retention by plan type (Medicare Advantage, Medigap, Part D) and carrier to identify churn drivers. Cross-referencing compliance and quality data (Voice-AI compliance flags, agent QA scores) with conversion outcomes to surface where compliance friction is reducing throughput. Building cohort views by lead source, agent, region, plan type, and Open Enrollment vs. Special Election Period to detect seasonal and structural patterns.
Translate Ambiguous Business Goals into Structured Problem Statements and Test Plans
Working with the COO and senior leadership to take open-ended directives ("improve retention," "lower CAC," "raise agent productivity") and decompose them into measurable subproblems with explicit hypotheses. Leading scoping sessions with stakeholders across Growth, Sales, Marketing, Product, Engineering, Operations, and Agency to align on success criteria, target metrics, scope boundaries, and implementation owners before analytical work begins. Drafting structured test plans that specify the target metric, control conditions, expected effect size, sample size, statistical significance threshold, and decision rule (roll out / iterate / kill). Documenting analytical assumptions, constraints, and known limitations so leadership can make informed go/no-go decisions on each initiative.
Identify Margin Expansion, Conversion Optimization, and Cost Efficiency Levers
Building margin and unit-economics models segmented by lead source, marketing channel, agent team, plan type, and carrier. Quantifying the dollar impact of conversion-rate improvements (e.g., revenue lift from a one-point increase in lead-to-enrollment) to prioritize where analytics investment will produce the highest ROI. Identifying high-cost-low-conversion lead segments and recommending reallocation of marketing spend or routing changes. Analyzing agent productivity (enrollments per agent-hour, revenue per agent, plan-match rate) to recommend training, scheduling, or workflow changes that lift output without adding headcount. Partnering with finance to reconcile analytical outputs against MPA's commission model and carrier payment cycles. Recommending process automation opportunities — AI-driven plan matching, automated QA, Voice-AI compliance — where data shows manual work is creating cost leakage.
Design Scalable Experimentation and Measurement Frameworks
Building MPA's experimentation backbone for consumer-facing acquisition and enrollment funnels — test definition, treatment assignment, exposure tracking, result analysis, decision documentation. Owning MPA's source-of-truth metric definitions across teams — what counts as a qualified lead, an enrollment, a retention event, an attribution conversion — so Growth, Marketing, Sales, and Operations all measure against the same definitions. Designing A/B test infrastructure for landing-page variants, call routing logic, agent scripts, and follow-up cadence so the company can run multiple concurrent tests without confounding effects. Establishing guardrails (compliance, sample size, statistical significance) that let non-analytics teams run experiments responsibly without analytical mistakes. Maintaining a reusable library of measurement assets (dashboards, queries, models, dbt-style metric layers) so future analyses build on shared infrastructure rather than starting from scratch.
Leverage AI Tools to Accelerate Modeling, Root-Cause Analysis, and Operational Automation
Integrating outputs from MPA's proprietary Voice-AI (real-time compliance monitoring and sales coaching) with conversion, retention, and revenue data to identify behavioral patterns that predict enrollment outcomes. Using AI-assisted modeling and scenario tools to accelerate root-cause analysis when KPIs move unexpectedly week-over-week or month-over-month. Partnering with the HealthyLabs team to apply AI-driven lead scoring and intelligent routing logic to MPA's acquisition funnel. Identifying operational analytics tasks (reporting, anomaly detection, exception flagging, executive narrative drafting) that can be partly or fully automated with AI tooling, and building the underlying logic. Evaluating, prototyping, and rolling out emerging AI tools for analytical workflows (modeling assistants, code generation for SQL and Python, automated report drafting).
Build Analytical Assets and Present Executive-Ready Recommendations
Building and maintaining executive dashboards covering acquisition, conversion, and retention KPIs across MPA's brokerage and distribution platform businesses. Producing quarterly and ad-hoc deep-dive analyses for the COO, CEO, and senior leadership team on strategic questions — channel mix, carrier mix, agent productivity, retention drivers, Annual Open Enrollment performance. Translating analytical findings into one-page executive summaries with clear recommendations, projected financial impact, and decision options. Presenting at leadership meetings and serving as the quantitative voice in strategic planning, board prep, and investor-facing analysis. Maintaining the analytical asset library (forecasting models, attribution models, cohort dashboards) used by Growth, Sales, Marketing, and Operations leadership to inform day-to-day decisions.
Bachelor's Degree is required in Data Science or Computer Science or Information Systems or Management Information Systems .
Full job record
| Job ID | 863a86338e303fa7ffee161cb0f8dc81c0664032 |
| Org ID | 609e5034-ee1f-4829-8411-ccf3f3a2b65b |
| Source ID | 79ecfadc-5542-46be-a8fa-492a1b77ca48 |
| Board ID | 79ecfadc-5542-46be-a8fa-492a1b77ca48 |
| Provider | rippling |
| Provider Job Key | 48a37fec-b7ef-4f38-b78a-a2304efa2e3e |
| Title | Senior Strategy and Analytics Lead |
| Normalized Title | — |
| Status | deleted |
| Active | no |
| Location Text | Remote (United States), United States |
| Department | Growth |
| Team | — |
| Employment Type | full_time |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | — |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://ats.rippling.com/myplanadvocate/jobs/48a37fec-b7ef-4f38-b78a-a2304efa2e3e |
| Apply URL | https://ats.rippling.com/myplanadvocate/jobs/48a37fec-b7ef-4f38-b78a-a2304efa2e3e |
| First Seen At | 2026-05-29 07:10:28Z |
| Last Seen At | 2026-06-03 12:19:05Z |
| Last Checked At | 2026-06-06 08:45:03Z |
| Last Changed At | 2026-06-06 08:45:03Z |
| Inactive At | 2026-06-06 08:45:03Z |
| Source Posted At | 2026-05-06 12:20:57Z |
| Source Updated At | — |
| Raw Payload Uri | s3://bluework-jobs-prod-raw-590183727216/raw/provider=rippling/board=myplanadvocate/date=2026-06-03/2026-06-03T12-19-04-828Z-ca5d35f2d25a9d6399436197a2145b9a644bb3df97c94f8fed87cfeaeb07470a.json |
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"role": "<meta><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:start;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11.5pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:start;\"><b><strong style=\"font-size:11.5pt;white-space:pre-wrap;\">Senior Strategy and Analytics Lead is needed to perform the following duties:</strong></b><span style=\"white-space:pre-wrap;\"> </span><br><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Funnel Analysis across Marketing, Sales, Operations, Product, and Compliance Systems</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Analyzing conversion across MPA's Medicare beneficiary acquisition funnel — from initial lead capture through licensed-agent telephonic intake, Needs Assessment, plan recommendation, and enrollment submission to the carrier.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Investigating drop-off points across paid acquisition, partner channels, and direct brand traffic to identify which sources produce leads with the highest end-to-end enrollment conversion.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Partnering with the licensed-agent operations team to analyze call-center metrics — speed-to-lead, talk time, completion rate, plan-match accuracy — and connecting those operational metrics back to conversion.</span></li><li style=\"font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Examining post-enrollment retention by plan type (Medicare Advantage, Medigap, Part D) and carrier to identify churn drivers.</span></li><li style=\"font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Cross-referencing compliance and quality data (Voice-AI compliance flags, agent QA scores) with conversion outcomes to surface where compliance friction is reducing throughput.</span></li><li style=\"font-size:10pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Building cohort views by lead source, agent, region, plan type, and Open Enrollment vs. Special Election Period to detect seasonal and structural patterns.</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Translate Ambiguous Business Goals into Structured Problem Statements and Test Plans</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\"> Working with the COO and senior leadership to take open-ended directives (\"improve retention,\" \"lower CAC,\" \"raise agent productivity\") and decompose them into measurable subproblems with explicit hypotheses.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Leading scoping sessions with stakeholders across Growth, Sales, Marketing, Product, Engineering, Operations, and Agency to align on success criteria, target metrics, scope boundaries, and implementation owners before analytical work begins.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Drafting structured test plans that specify the target metric, control conditions, expected effect size, sample size, statistical significance threshold, and decision rule (roll out / iterate / kill).</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Documenting analytical assumptions, constraints, and known limitations so leadership can make informed go/no-go decisions on each initiative.</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><span style=\"font-size:10pt;white-space:pre-wrap;\"> </span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Identify Margin Expansion, Conversion Optimization, and Cost Efficiency Levers</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Building margin and unit-economics models segmented by lead source, marketing channel, agent team, plan type, and carrier.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Quantifying the dollar impact of conversion-rate improvements (e.g., revenue lift from a one-point increase in lead-to-enrollment) to prioritize where analytics investment will produce the highest ROI.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Identifying high-cost-low-conversion lead segments and recommending reallocation of marketing spend or routing changes.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Analyzing agent productivity (enrollments per agent-hour, revenue per agent, plan-match rate) to recommend training, scheduling, or workflow changes that lift output without adding headcount.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Partnering with finance to reconcile analytical outputs against MPA's commission model and carrier payment cycles.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Recommending process automation opportunities — AI-driven plan matching, automated QA, Voice-AI compliance — where data shows manual work is creating cost leakage.</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><span style=\"font-size:10pt;white-space:pre-wrap;\"> </span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Design Scalable Experimentation and Measurement Frameworks</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Building MPA's experimentation backbone for consumer-facing acquisition and enrollment funnels — test definition, treatment assignment, exposure tracking, result analysis, decision documentation.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Owning MPA's source-of-truth metric definitions across teams — what counts as a qualified lead, an enrollment, a retention event, an attribution conversion — so Growth, Marketing, Sales, and Operations all measure against the same definitions.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Designing A/B test infrastructure for landing-page variants, call routing logic, agent scripts, and follow-up cadence so the company can run multiple concurrent tests without confounding effects.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Establishing guardrails (compliance, sample size, statistical significance) that let non-analytics teams run experiments responsibly without analytical mistakes.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Maintaining a reusable library of measurement assets (dashboards, queries, models, dbt-style metric layers) so future analyses build on shared infrastructure rather than starting from scratch.</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:10pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><span style=\"font-size:10pt;white-space:pre-wrap;\"> </span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Leverage AI Tools to Accelerate Modeling, Root-Cause Analysis, and Operational Automation</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Integrating outputs from MPA's proprietary Voice-AI (real-time compliance monitoring and sales coaching) with conversion, retention, and revenue data to identify behavioral patterns that predict enrollment outcomes.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Using AI-assisted modeling and scenario tools to accelerate root-cause analysis when KPIs move unexpectedly week-over-week or month-over-month.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Partnering with the HealthyLabs team to apply AI-driven lead scoring and intelligent routing logic to MPA's acquisition funnel.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Identifying operational analytics tasks (reporting, anomaly detection, exception flagging, executive narrative drafting) that can be partly or fully automated with AI tooling, and building the underlying logic.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Evaluating, prototyping, and rolling out emerging AI tools for analytical workflows (modeling assistants, code generation for SQL and Python, automated report drafting).</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><span style=\"white-space:pre-wrap;\"> </span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><b><strong style=\"white-space:pre-wrap;\">Build Analytical Assets and Present Executive-Ready Recommendations</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Building and maintaining executive dashboards covering acquisition, conversion, and retention KPIs across MPA's brokerage and distribution platform businesses.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Producing quarterly and ad-hoc deep-dive analyses for the COO, CEO, and senior leadership team on strategic questions — channel mix, carrier mix, agent productivity, retention drivers, Annual Open Enrollment performance.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Translating analytical findings into one-page executive summaries with clear recommendations, projected financial impact, and decision options.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Presenting at leadership meetings and serving as the quantitative voice in strategic planning, board prep, and investor-facing analysis.</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;text-align:justify;\"><span style=\"font-size:11.5pt;white-space:pre-wrap;\">Maintaining the analytical asset library (forecasting models, attribution models, cohort dashboards) used by Growth, Sales, Marketing, and Operations leadership to inform day-to-day decisions.</span></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:start;\"><b><strong style=\"white-space:pre-wrap;\">Bachelor's Degree is required in </strong></b><b><strong style=\"color:black;font-size:11.5pt;white-space:pre-wrap;\">Data Science or Computer Science or Information Systems or Management Information Systems</strong></b><b><strong style=\"white-space:pre-wrap;\">.</strong></b></p>",
"company": "<meta><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">Join Our Mission</strong></b></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">Healthy Labs and </span><b><strong style=\"white-space:pre-wrap;\">MyPlanAdvocate</strong></b><span style=\"white-space:pre-wrap;\"> are on a mission to transform how consumers connect with insurance and healthcare solutions. We serve often-overlooked populations and believe that better data, better systems, and better decision-making can materially improve lives.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">Healthy Labs operates as our high-scale proving ground for Medicare and insurance distribution. MyPlanAdvocate is the AI-native platform powering our long-term vision: an end-to-end growth, compliance, and operations engine for agencies across insurance verticals. </span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">As part of the MPA portfolio, you will work at the intersection of growth, analytics, AI, and regulated marketplaces — helping shape how modern insurance distribution is built.</span></p>"
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