[Hiring] Senior Strategy and Analytics Lead @MyPlanAdvocate

Role Description

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.

Qualifications

• Bachelor's Degree in Data Science, Computer Science, Information Systems, or Management Information Systems.