As a Senior Data Scientist, 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 multi disciplinary 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.
Having 10 or more years of experience, this position will be responsible for leading and supporting data science initiatives across the full analytics lifecycle.
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.
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).
Excellent written and verbal communication skills.
Experience with BI and visualization tools such as Power BI, Tableau, or SAS.
Understanding of data modeling optimization for MPP platforms, including distribution keys and schemes.
Experience pursuing replicable causal inference using observational or repurposed data.
Prior experience performing advanced analytics on healthcare data.
Knowledge of functional programming concepts.
Post graduate coursework or training in a healthcare related discipline.
For 50 years, our nation’s federal Medicaid program has worked to improve the health, safety and well-being of America’s most vulnerable populations: low-income families, women and children, seniors, and those with disabilities. With positive health and cost outcomes that pierce inequities and impact economies, the success of these programs is inextricably tied to the prosperity of communities, individual states and the nation as a whole. We think that demands respect and, more importantly, is deserving of a lifetime commitment from innovators who can help those who operate within and around health and human services evolve — in any market at any stage. At Gainwell Technologies, that’s our sole focus.
Built across more than five decades, Gainwell has intentionally seized opportunities to advance its digitally enabled services to meet agencies, health plans and MCOs where they are on their modernization journeys and propel them into the future of public health. Our commitment to innovation, deep experience and ability to leverage insights from customers across 50 states has allowed us to expand on next-generation, cloud-enabled technologies.
Today, Gainwell offers one of the most comprehensive suites of scalable services and solutions on the market — all proven to deliver cost savings, better patient outcomes and an improved provider experience. Equally important to our expanding technologies and results: We bring ideas that bring policies to life.
