Lead Informaticist, Medicaid Pharmacy Forecasting

CenterWell

8d • $117,600 - $161,700 • Remote

About The Position

The Lead Informaticist, Medicaid Pharmacy Forecasting owns the drug- and market-level utilization forecast for every Medicaid state Humana supports. This role is responsible for producing a forecast that is robust, defensible, and decision-ready—and for clearly explaining to executive stakeholders where variance is coming from when actuals diverge from expectations. This is a hands-on quantitative role with significant cross-functional visibility. The Lead Informaticist personally designs the forecasting methodology, builds and maintains the models, accounts for the operational realities of Medicaid (state-by-state data differences, cohort dynamics, new-state launches, benefit and formulary changes, regulatory shifts), and translates results into executive-ready narratives that leaders use to plan, intervene, and communicate with internal and state partners. The role operates in a Databricks environment and is expected to leverage AI agents and modern coding tools to accelerate model iteration, scenario analysis, and explanatory analytics.

Requirements

• Bachelor's degree (or equivalent experience) in a quantitative discipline (Statistics, Economics, Data Science, Operations Research, Mathematics, Actuarial Science, Health Services Research, or related); advanced degree preferred.
• 5+ years of progressive quantitative analytics experience, with 3+ years specifically in forecasting (utilization, demand, financial, or comparable).
• Demonstrated experience producing drug-, product-, or market-level forecasts in a healthcare, pharmacy, payer, PBM, or comparable setting.
• Strong hands-on proficiency in Python, PySpark, and/or SQL, with the ability to build and maintain reproducible forecasting pipelines.
• Working knowledge of forecasting methods across classical time series, regression-based, and machine learning approaches; ability to choose and defend the right method for the problem.
• Demonstrated experience explaining forecast variance to non-technical executives in clear, decomposable terms.
• Comfort with leveraging AI agents and coding tools to accelerate analysis and iteration.
• Strong written and verbal communication skills, with a track record of translating quantitative work into executive-ready narratives.

Nice To Haves

• Experience working in Databricks or comparable lakehouse environments.
• Direct experience with Medicaid pharmacy data and an understanding of state-by-state operational, regulatory, and data realities.
• Familiarity with handling cold-start / new-market launches (e.g., hierarchical models, lookalike approaches, Bayesian shrinkage).
• Experience with uncertainty quantification (prediction intervals, Bayesian methods, scenario modeling).
• Familiarity with pharmacy-specific dynamics: launches, LOEs, biosimilars, GLP-1 category disruption, formulary/PDL change impacts, and PA policy effects.
• Experience standing up standing variance/attribution analytics that explain "what changed and why" each cycle.
• Track record of partnering directly with finance, actuarial, clinical, and market leadership teams.

Responsibilities

• Own Medicaid Drug- and Market-Level Forecasting End-to-End: Design, build, and maintain forecasts for drug-level utilization (script counts, days supply, cost, mix) and market-level utilization for each Medicaid state Humana supports. Produce forecasts at the cadence required by the business (e.g., monthly refresh, ad-hoc scenarios, launch projections, annual planning). Maintain a forecasting framework that is transparent, reproducible, and version-controlled—so results are traceable and defensible to executive and partner audiences.
• Account for the Realities of Medicaid: Explicitly handle state-by-state differences in: Data availability, completeness, and lag; Member cohort composition, eligibility patterns, churn, and risk mix; Benefit design, formulary, PDL, and prior authorization policies; Provider, pharmacy network, and dispensing patterns; Regulatory and reimbursement environment (FFS vs. MCO, carve-in/carve-out, supplemental rebate dynamics). Build forecasting approaches that accommodate new-state launches—including ramp curves, cold-start handling, lookalike methods, and Bayesian shrinkage or hierarchical approaches when state-specific history is thin or absent. Account for drug-specific dynamics: new launches, LOEs/generic entrants, biosimilar uptake, indication expansions, GLP-1 and other category-level disruptions, and seasonality.
• Build Methodologically Sound, Robust Forecasts: Select and apply the right method for the problem—e.g., classical time series (ARIMA, ETS, state-space), hierarchical and panel models, regression-based decomposition, machine learning (gradient boosting, regularized regression), Bayesian hierarchical models, and ensembles—with clear justification for the chosen approach. Quantify and communicate uncertainty (intervals, scenarios, sensitivity) rather than presenting point estimates alone. Stress-test forecasts against historical analogs, holdout periods, and reasonable counterfactuals; document assumptions explicitly. Establish and monitor forecast accuracy metrics (e.g., MAPE, WAPE, bias, calibration) at appropriate levels of granularity, and continuously improve methodology based on observed performance.
• Variance Explanation & Executive Communication: When actuals deviate from forecast, diagnose and clearly explain the drivers of variance to executive stakeholders—decomposing variance into intuitive components such as: Membership / cohort change; Mix shift (drug, category, channel, state); Unit cost / rate change; Utilization rate change; Launches, LOEs, policy changes, and one-time events. Build standing variance and attribution analytics so leaders see what changed, why it changed, and what it means every cycle—not just what the number is. Translate technical results into concise executive narratives that anticipate the questions VPs and SVPs will ask.
• Partner with Stakeholders and Drive Decisions: Partner with clinical strategy, pricing, network, finance, actuarial, Medicaid market leadership, and state-facing teams to ensure forecasts reflect the best available business intelligence and operational reality. Support new-state launch readiness by producing pre-launch forecasts, sensitivity ranges, and post-launch tracking against expectations. Translate forecast insights into clear options and recommended actions—e.g., where to intervene, where to escalate, where to adjust assumptions—so leaders can act, not just observe.
• AI-Accelerated Forecasting & Tooling: Leverage AI agents, copilots, and modern coding tools to accelerate model development, feature engineering, code review, scenario testing, and explanatory analytics. Operate hands-on in Databricks using Python, PySpark, and/or SQL, with reproducible pipelines and clear documentation. Establish good engineering hygiene for the forecasting codebase: parameterization, configuration, testing, and reusable components that support extensibility as new states, drugs, and scenarios are added.
• Elevate the Practice: Document methodology, assumptions, and known limitations clearly so the forecast is understandable and maintainable by others. Mentor more junior analysts on forecasting technique, variance decomposition, and executive communication. Stay current on changes in Medicaid policy, and pharmacy market dynamics, and translate developments into forecast improvements.

Benefits

• medical, dental and vision benefits
• 401(k) retirement savings plan
• time off (including paid time off, company and personal holidays, paid parental and caregiver leave)
• short-term and long-term disability
• life insurance
• many other opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Senior