Senior Data Scientist

What you'll do - Generate medical insights from Atria's longitudinal clinical data: surface early signals of disease, characterize how members respond to interventions, and identify population-level trends clinicians can act on. - Build forecasting models on clinical and operational signals: disease progression, biomarker trajectories, member engagement, demand and capacity planning, and other time-series problems. - Lead or contribute to operational analytics: visit patterns, throughput and scheduling, panel composition, vendor performance, member journeys, and the quiet efficiency questions that compound over a year. - Design and execute studies in partnership with clinicians: clear questions, well-controlled comparisons, honest error analysis, and writeups that hold up to clinical scrutiny. - Design and analyze experiments (A/B tests, staged rollouts, quasi-experiments) with appropriate attention to power, multiple comparisons, and the difference between "statistically significant" and "actually mattered". - Author and maintain curated views and dbt models in our Snowflake gold layer, documented well enough that clinicians and operational staff can use them confidently without a Slack DM to you every Tuesday. - Build dashboards and analytical tools in Omni Analytics that clinicians and operational leaders actually use to make decisions. - Partner with the AI Scientists and AI Engineers on the boundary where analytical work meets model training and production systems: contributing ground truth, evaluation datasets, and the clinical framing that keeps their models pointed at the right problem. - Translate clinical and operational questions into well-formed analytical problems, and translate results back into clear recommendations.

The translation in both directions is most of the job.

Requirements - A bias to deliver. You would rather have a clear answer to a real question in front of a clinician next week than a beautiful methodology that ships next quarter. - A scrappy streak. You can pick up an unfamiliar dataset, statistical method, or clinical concept on a Wednesday and have a credible first cut by Friday. - A serious drive to keep getting better.

You read other people's analyses, papers, and code. You treat being wrong as cheap information rather than an event requiring counseling. - Experience in data science, analytics, or applied statistics: 5+ years, with at least 2 in healthcare, biomedical, or another regulated longitudinal-data domain. - Strong foundation in statistics and probability: you can defend a confidence interval, a regression specification, and a sensible forecasting baseline honestly. - Solid working knowledge of forecasting and time-series methods (classical and modern), regression modeling, and applied ML for tabular data (gradient boosting and related). - Familiarity with the design and analysis of experiments and quasi-experiments: enough to know when an A/B test is the right tool, when it isn't, and what to do about it. - Strong Python (pandas, scikit-learn, statsmodels, and at least one forecasting library) and SQL that you write without apologizing for. - Comfort with a cloud warehouse (Snowflake preferred) and modern data tooling such as dbt and a workflow orchestrator (Dagster, Airflow). - Track record of partnering with domain experts and turning their questions into defensible analytical work: clinicians, ops leaders, or comparable stakeholders. - Clear written and visual communication: tight executive summaries, defensible methods sections, and charts that have titles, axes, and a point. - Interest in healthcare and the responsibility that comes with working on data that affects patient care.

Nice to have - Graduate degree in a quantitative field (statistics, biostatistics, epidemiology, computer science, applied math, or a relevant clinical/biological science), or a strong track record of analytical work in lieu of formal credentials. - Experience with causal inference methods. - Experience with modern forecasting toolkits and hierarchical or probabilistic forecasting. - Familiarity with modern deep learning tools (PyTorch, Hugging Face ecosystem): enough to collaborate productively with AI Scientists and Engineers. - Familiarity with a BI tool (Omni, Looker, Mode, Tableau). - Published peer-reviewed clinical, biomedical, or operations research. - Any prior work in healthcare, biology, or another regulated domain.

Salary range: $180,000 $260,000 Base Salary + performance-based bonus Benefits At Atria, we are proud to offer every member of the Atria team: - Excellent health and wellness benefits, fully covered by Atria, effective date of hire - OneMedical membership for employees & dependents, giving access to 24/7 virtual care - Fertility & family planning - Company-covered preventive health screenings through partner hospitals (calcium score) - Fitness Perks, including Wellhub + - 401k contributions and 4% match starting after 6 months