Staff Forecasting Data Scientist

Omada Health, Inc.

United States

about 1 month ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

3 years minimum

Compensation

$193,600.0 - $253,000.0

Working hours

Regular working hours

Job source

job-boards.greenhouse.io

Tech stack

Artificial Intelligence Data Analysis Software as a Service Customer Data Management Information Engineering EHealth Generalized Linear Model Python (Programming Language) Machine Learning NumPy Operational Databases Backtesting

+10 more

SQL Databases Management of Software Versions Prophet Pandas Scikit Learn Statistics Packages Xgboost Performance Monitor Data Management Software Coding

Job description

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

Requirements

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
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
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
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
Experience designing and maintaining production data science systems in partnership with data engineering and platform teams, including versioning, backtesting, performance monitoring, and alerting
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
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
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
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