Data Scientist
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Job Description
Senior Data Scientist at Gainwell, leading data analytics initiatives across healthcare datasets. Collaborating with cross-functional teams to generate actionable insights within a complex healthcare landscape.
Responsibilities:
- Lead data ingestion, cleansing, transformation, and aggregation efforts for large scale and complex datasets.
- Design and implement advanced feature engineering, statistical estimation, and hypothesis testing techniques.
- Develop, validate, and refine machine learning and statistical models, including time series, repeated measures, and mixed effects models.
- Ensure analytical rigor by addressing overfitting, false discovery, bias, and model generalizability.
- Analyze healthcare and enterprise datasets to surface complex, high impact, actionable insights that support strategic decision making.
- Drive iterative model development and support continuous integration and deployment of analytics solutions.
- Optimize data science solutions for performance, scalability, and production readiness.
- Leverage cloud based platforms to support elastic, high volume data science workloads.
- Collaborate with business stakeholders, data engineers, architects, and analysts to align analytics outputs with business objectives.
- Provide technical leadership and guidance to junior data scientists and analysts.
- Contribute to the definition and evolution of data science standards, best practices, and reusable analytics assets.
Requirements:
- 10+ years of experience in data science, advanced analytics, or related roles.
- Expert proficiency in SQL, including complex set based query development for large scale datasets.
- Strong understanding of database concepts such as indexing, stored procedures, and materialized views.
- Advanced proficiency in Python, including object oriented design and common machine learning libraries.
- Strong knowledge of statistical methods, including time series analysis, repeated measures, mixed effects models, and hypothesis testing.
- Proven experience applying machine learning techniques, including model evaluation, tuning, and lifecycle management.
- Experience with Dev/Sec/Ops practices and CI/CD pipelines for analytics development and deployment.
- Strong experience in performance optimization for both development and production analytics environments.
- Hands on experience using Databricks for enterprise data science workloads; Scala knowledge is a plus.
- Knowledge of semi structured and unstructured data, schema on read techniques, parsers, and NLP libraries.
- Demonstrated experience deriving insights from healthcare datasets.
- Experience performing data science in a major cloud environment (AWS, Azure, or GCP).
Benefits:
- Health insurance
- Retirement plans
- Professional development opportunities
- Flexible work arrangements
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