Senior Data Scientist

About the role

The Senior Data Scientist at Atria Health Institute will leverage Atria's unique longitudinal dataset to generate medical insights and forecasts that inform clinical care and operational efficiency.

Responsibilities

• Generate medical insights from Atria's longitudinal clinical data: surface early signals of disease, characterize member responses 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"
• 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

Requirements

• A bias to deliver: clear answers to real questions in front of clinicians next week
• A scrappy streak: picking up unfamiliar datasets, methods, or concepts quickly
• A serious drive to get better: reading others' analyses, papers, and code, treating being wrong as valuable information
• Strong foundation in statistics and probability: defending confidence intervals, regression specifications, and forecasting baselines honestly
• Solid working knowledge of forecasting and time-series methods, regression modeling, and applied ML for tabular data
• Familiarity with experimental design and quasi-experimental methods: knowing when and how to apply them
• Comfort with Python (pandas, scikit-learn, statsmodels, and at least one forecasting library) and SQL
• Experience with a cloud warehouse (Snowflake preferred) and modern data tooling like dbt and a workflow orchestrator (Dagster, Airflow)
• Track record of partnering with domain experts and turning their questions into analytically sound work
• Clear written and visual communication skills: producing tight executive summaries, robust methods sections, and informative charts

Qualifications

• 5+ years of experience in data science, analytics, or applied statistics, with at least 2 in healthcare, biomedical, or another regulated longitudinal-data domain
• 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, modern forecasting toolkits, and deep learning tools
• Familiarity with BI tools (Omni, Looker, Mode, Tableau)

Skills

• Python (pandas, scikit-learn, statsmodels, and at least one forecasting library)
• SQL
• Cloud warehouse (Snowflake preferred)
• Data tooling (dbt, Dagster, Airflow)
• BI tool (Omni, Looker, Mode, Tableau)

Benefits

• Excellent health and wellness benefits
• OneMedical membership for employees & dependents
• Company-covered preventive health screenings
• Wellness perks
• 401(k) contributions and 4% match starting after 6 months