Staff Forecasting Data Scientist

Omada Health

10d•$193,600 - $253,000•Remote

About The Position

Omada Health is looking for a Staff Forecast Data Scientist to lead the technical development and automation of our enrollment forecasting capability. This role will build and scale forecasting models for our existing book of business, helping transform a historically manual, assumption-heavy process into a robust, production-oriented forecasting engine that improves forecast quality, consistency, and speed. Working closely with Commercial Operations, Finance, Data Engineering, and Applied Statistics, this person will design, deploy, and monitor forecasting solutions that support critical planning and decision-making across the business. The ideal candidate combines strong technical depth in time-series and predictive modeling with the ability to operate in ambiguous business contexts, translate commercial questions into scalable analytical solutions, and provide technical leadership in how forecasting is built at Omada.

Requirements

8+ years of experience in data science or applied statistics roles, with at least 3 years focused on forecasting, time series modeling, or revenue/enrollment prediction in a SaaS, healthcare, or similar recurring-revenue business.
Deep hands-on proficiency in Python (e.g., pandas, numpy, scikit-learn, statsmodels, Prophet or similar libraries) and SQL, with a track record of taking models from discovery through deployment and ongoing monitoring.
Strong grounding in statistical and machine learning methods for forecasting (e.g. hierarchical or panel forecasting, gradient boosting, generalized linear models), and a practical sense for when simple models outperform complex ones.
Experience designing and maintaining production data science systems in partnership with data engineering and platform teams, including versioning, backtesting, performance monitoring, and alerting.
Comfort working with messy, real-world commercial data (CRM, marketing, product/event, and financial data) and building robust pipelines and features that can support recurring forecast runs.
Demonstrated ability to translate ambiguous business questions into well-scoped technical problems, communicate tradeoffs clearly to non-technical stakeholders, and incorporate feedback into model and metric design.
Proven experience influencing cross-functional partners (e.g., Commercial Operations, Sales, Marketing, Finance) using data-driven insights, including framing uncertainty, risk, and scenario ranges in an executive-friendly way.
High degree of ownership and bias toward action: willing to dive into data, prototypes, and code while also stepping back to design scalable systems and long-term improvements to forecasting capabilities.
Comfortable working in a fast‑changing environment where GTM motions, products, and partner needs evolve quickly, and where you help drive clarity through structure, process, and analytics.

Nice To Haves

Experience implementing or upgrading forecasting tools, analytical workflows, or data models in a high‑growth, evolving, or public‑company environment.
Background in healthcare, digital health, health plans/PBMs, or other complex, regulated industries with multi‑stakeholder sales cycles.
Prior work supporting capacity planning or operational forecasting alongside care delivery, supply chain, or customer support teams.
Familiarity with Salesforce data models and RevOps processes (pipeline management, incentive compensation, territory / quota design).
Passion for leveraging data, analytics, and emerging technologies (e.g., advanced BI, AI‑driven forecasting) to improve healthcare and outcomes for people living with chronic conditions.

Responsibilities

Design, build, and automate Omada’s core enrollment forecasting engine for the existing book of business, significantly reducing manual effort and increasing forecast reliability and reproducibility.
Translate commercial planning questions into scalable forecasting solutions, partnering closely with Commercial Operations, Sales, Marketing, and Finance to ensure the models reflect real-world dynamics and are usable in day-to-day decision making.
Establish and own best practices for model development, backtesting, performance monitoring, and alerting for enrollment forecasts, helping Omada move from one-off analyses to a robust, production-grade forecasting capability.
Improve forecast accuracy and responsiveness over time by continuously experimenting with new data sources, features, and modeling techniques, and systematically incorporating learnings from forecast performance.
Act as the primary technical leader for forecasting within the Data organization, providing guidance on tooling, coding standards, and architecture, and mentoring other data scientists who contribute to forecasting projects.
Free Commercial Operations leadership to focus on product-line strategy and new go-to-market motions by taking ownership of the technical implementation of base forecasting, while collaborating closely on the assumption framework and narrative.