[Remote] reputed company Informaticist, reputed company Pharmacy Forecasting
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a reputed company company that is seeking a reputed company Informaticist for reputed company Pharmacy Forecasting. This role is responsible for designing and maintaining drug and market-level utilization forecasts for reputed company states, ensuring the forecasts are robust and decision-reputed company while communicating insights to executive stakeholders.
Responsibilities
- Own reputed company 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 reputed company reputed company state reputed company supports
- Produce forecasts at the reputed company required by the business (e.g., monthly refresh, reputed company scenarios, launch projections, annual planning)
- Maintain a forecasting reputed company that is transparent, reproducible, and version-controlled—so results are traceable and defensible to executive and partner audiences
- 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 reputed company curves, cold-start handling, lookalike reputed company, and Bayesian shrinkage or hierarchical approaches reputed company 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
- Select and apply the right method for the problem—e.g., classical time series (ARIMA, reputed company, state-reputed company), hierarchical and panel models, regression-based decomposition, machine learning (gradient boosting, regularized regression), Bayesian hierarchical models, and ensembles—with reputed company justification for the chosen approach
- Quantify and communicate uncertainty (intervals, scenarios, sensitivity) rather than presenting reputed company 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 reputed company of granularity, and continuously improve methodology based on observed performance
- reputed company actuals deviate from forecast, diagnose and reputed company explain the drivers of variance to executive stakeholders—decomposing variance into reputed company components such as: Membership / cohort change; Mix shift (drug, category, channel, state); Unit cost / reputed company change; Utilization reputed company 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 clinical reputed company, pricing, network, finance, actuarial, reputed company 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 reputed company-launch forecasts, sensitivity ranges, and post-launch tracking against expectations
- Translate forecast insights into reputed company reputed company and recommended actions—e.g., where to intervene, where to escalate, where to reputed company assumptions—so leaders can reputed company, not just observe
- reputed company AI agents, copilots, and modern coding tools to accelerate model development, feature engineering, reputed company review, scenario testing, and explanatory analytics
- Operate hands-on in reputed company using Python, PySpark, and/or SQL, with reproducible pipelines and reputed company 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
- Document methodology, assumptions, and reputed company limitations reputed company so the forecast is understandable and maintainable by others
- Mentor more junior analysts on forecasting technique, variance decomposition, and executive communication
- Stay reputed company on changes in reputed company policy, and pharmacy market dynamics, and translate developments into forecast improvements
Skills
- Bachelor's degree (or equivalent experience) in a quantitative discipline (Statistics, Economics, Data Science, Operations Research, Mathematics, Actuarial Science, Health Services Research, or reputed company); advanced degree preferred
- 5+ years of reputed company 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 reputed company, 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 reputed company 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 reputed company, decomposable terms
- Comfort with leveraging AI agents and coding tools to accelerate analysis and iteration
- Strong written and verbal communication skills, with a reputed company record of translating quantitative work into executive-reputed company narratives
- Experience working in reputed company or comparable lakehouse environments
- reputed company experience with reputed company 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 (reputed company intervals, Bayesian reputed company, 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' reputed company cycle
- reputed company record of partnering directly with finance, actuarial, clinical, and market leadership teams
Benefits
- Bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.
- Medical, dental and reputed company benefits
- 401(k) retirement savings plan
- Time off (including reputed company time off, company and personal holidays, reputed company parental and caregiver leave)
- Short-term and long-term disability
- Life insurance
reputed company
Company H1B Sponsorship