The job, in the employer's words
The Atria Health Institute is a membership-based primary and specialty health care practice with a focus on prevention and longevity. We bring together a multidisciplinary team of renowned physicians to provide proactive, preventive, and precision-based care for Atria members and their families. All care, including primary care, advanced screening and diagnostics, urgent care, specialty care, 24/7 home visits, and imaging is included in members’ annual fee.
Our mission is to make healthspan and lifespan equal for all by translating science into medicine in real-time, all while bringing humanity back into health care. Delivering such robust, personalized, and preventive health care is complex and requires a team-wide dedication to excellence.
We have state-of-the-art facilities with imaging and diagnostics on-site in New York City, Palm Beach, and Los Angeles, with home services hubs in Miami, Greenwich/Westchester, and the Hamptons in summer. Additional locations are forthcoming in the Bay Area, Miami, and in downtown Manhattan. At each of our Institutes, we deliver personalized preventive medicine grounded in up-to-the-minute science — all in serene environments designed for trust and comfort.
Role Overview
As a Senior Data Scientist at Atria, you will generate the medical insights and forecasts that shape how our clinicians care for members and how Atria runs as a practice. You'll work with one of the most distinctive longitudinal datasets in medicine: deep per-member baselines (whole-genome sequencing, advanced imaging, comprehensive labs, movement analysis, lifestyle inputs) revisited on cadence over years and linked across full family lineages, alongside the operational data of how a multi-disciplinary care team actually delivers that care. Your work spans both sides of that equation: surfacing clinically meaningful patterns clinicians can act on, and building the analyses, forecasts, and operational models that help Atria deliver better care more efficiently.
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
Requirements mentioned
- • 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.
- • 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
No Mondays shows this listing as the employer published it. Check the details on the employer's site before you apply.