Senior Data Scientist
New
A
Atria Health and Research InstituteHealthcare
United StatesFull-TimeSenior
Salary$180,000 $260,000 Base Salary + performance-based bonus
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningSnowflakeAirflowdbt
Requirements
- 5+ years of experience in data science, analytics, or applied statistics.
- At least 2 years of experience in healthcare, biomedical, or another regulated longitudinal-data domain.
- Strong foundation in statistics and probability.
- Working knowledge of forecasting, time-series methods, regression modeling, and applied ML for tabular data.
- Experience with the design and analysis of experiments and quasi-experiments.
- Strong Python skills (pandas, scikit-learn, statsmodels, forecasting libraries).
- Proficiency in SQL.
- Experience with a cloud warehouse, specifically Snowflake.
- Experience with modern data tooling such as dbt and workflow orchestrators like Dagster or Airflow.
- Track record of partnering with domain experts (clinicians, operations leaders) to turn questions into analytical work.
- Excellent written and visual communication skills.
- Graduate degree in a quantitative field is a plus.
Responsibilities
- Generate medical insights from Atria's longitudinal clinical data to surface disease signals and identify population-level trends.
- Build forecasting models on clinical and operational signals, including disease progression, biomarker trajectories, and capacity planning.
- Lead or contribute to operational analytics such as visit patterns, throughput, and member journeys.
- Design and execute studies in partnership with clinicians using well-controlled comparisons.
- Design and analyze experiments including A/B tests and staged rollouts with appropriate statistical rigor.
- Author and maintain curated data views and dbt models in Snowflake.
- Build dashboards and analytical tools in Omni Analytics for clinical and operational leaders.
- Collaborate with AI Scientists and Engineers on evaluation datasets and clinical framing for model development.
- Translate complex clinical and operational questions into well-formed analytical problems and actionable recommendations.
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