Data Scientist

Role Description

As a Data Scientist at Gainwell, you will support Gainwell's Medicaid and public sector analytics initiatives by leading advanced data science activities across complex healthcare and enterprise datasets. The role focuses on applying statistical modeling, machine learning, and scalable analytics techniques to generate actionable insights that inform business, clinical, and programmatic decisions.

You will function independently within a business area while collaborating across multidisciplinary teams. At this level, the Data Scientist is expected to influence analytical approaches, mentor junior staff, and contribute to the continuous improvement of Gainwell's data science practices, tooling, and delivery standards.

This role is strictly involved in data analytics, modeling, and advanced data science solution development and does not involve access to Protected Health Information (PHI), Personally Identifiable Information (PII), or any secured or confidential client data. The work is limited to analytics development, model design, and insight generation using approved and governed datasets and does not include handling or processing of sensitive health or personal information.

Your role in our mission:

• 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.
• Clearly document analytical methodologies, assumptions, results, and recommendations.
• Present insights and recommendations effectively to technical and non-technical stakeholders, including leadership audiences.

Qualifications

• 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.
• Deep, hands-on experience with SQL windowing functions.
• 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

• Remote working
• Work life balance
• Shift timing: 1pm to 10pm