Overview:
The Lead Data Scientist plays a strategic and technical leadership role in advancing Government Programs' Risk Adjustment initiatives through the application of advanced data science techniques, including machine learning, predictive modeling, and statistical inference. This senior position is responsible for designing and implementing scalable, end-to-end data science solutions that enhance risk score accuracy, support regulatory compliance, and drive financial performance across Medicare Advantage, ACA, and Medicaid programs.
Using Python and cloud-based analytics platforms, the Lead Data Scientist develops and manages machine learning models to forecast risk, identify outreach opportunities, and evaluate program effectiveness. The role requires deep expertise in healthcare data (e.g., claims, encounter, enrollment), strong knowledge of HCC risk adjustment methodologies, and the ability to translate complex analytical outputs into actionable insights for both technical and executive audiences.
As a thought leader, the Lead Data Scientist collaborates across clinical, actuarial, and operational teams, mentors junior data scientists, and fosters a data-driven culture to support innovation and strategic decision-making across the organization.


Job Summary:

This senior individual contributor is primarily responsible for leading the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats. This role is also responsible for leading the development of detailed problem statements outlining hypotheses and their effect on target clients/customers, serving as an expert in the analysis and investigation of complex data sets, leading the selection, manipulation and transformation of data into features used in machine learning algorithms, training statistical models, leading the deployment and maintenance of reliable and efficient models through production, verifying and ensuring model performance, and partnering with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Essential Responsibilities:

• Promotes learning in others by communicating information and providing advice to drive projects forward; builds relationships with cross-functional stakeholders. Listens, responds to, seeks, and addresses performance feedback; provides actionable feedback to others, including upward feedback to leadership and mentors junior team members. Practices self-leadership; creates and executes plans to capitalize on strengths and improve opportunity areas; influences team members within assigned team or unit. Adapts to competing demands and new responsibilities; adapts to and learns from change, challenges, and feedback. Models team collaboration within and across teams.


• Conducts or oversees business-specific projects by applying deep expertise in subject area; promotes adherence to all procedures and policies. Partners internally and externally to make effective business decisions; determines and carries out processes and methodologies; solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Develops work plans to meet business priorities and deadlines; coordinates and delegates resources to accomplish organizational goals. Recognizes and capitalizes on improvement opportunities; evaluates recommendations made; influences the completion of project tasks by others.


• Leads the development of detailed problem statements outlining hypotheses and their effect on target clients/customers by ensuring comprehensive and accurate definitions of scope, objectives, outcome statements and metrics.


• Leads the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by overseeing the transformation, cleansing, and storing of data for consumption by downstream processes; writing and optimizing diverse and complex SQL queries; and demonstrating expertise of database fundamentals.


• Serves as an expert in the analysis and investigation of complex data sets by ensuring optimum data visualization methods are employed; determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions; and reviewing and verifying summaries of key dataset characteristics.


• Leads the selection, manipulation, and transformation of data into features used in machine learning algorithms by leveraging and demonstrating expertise in techniques to conduct dimensionality reduction, feature importance, and feature selection.


• Trains statistical models by selecting and leveraging algorithms and data mining techniques; leading model testing by ensuring the proper use of various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.


• Leads the deployment and maintenance of reliable and efficient models through production.


• Verifies and ensures model performance by demonstrating advanced expertise in the practice of a variety of model validation techniques to assess and discriminate the goodness of model fit; and leveraging feedback and output to manage and strengthen model performance.


• Partners with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by generating and delivering insights and values from heterogeneous data to investigate complex problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical leadership.

Minimum Qualifications:

• Minimum three (3) years experience working with Exploratory Data Analysis (EDA) and visualization methods.


• Minimum five (5) years machine learning and/or algorithmic experience.


• Minimum five (5) years statistical analysis and modeling experience.


• Minimum five (5) years programming experience.


• Minimum three (3) years experience in a leadership role with or without direct reports.


• Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum eight (8) years experience in data science or a directly related field. Additional equivalent work experience in a directly related field may be substituted for the degree requirement. Advanced degrees may be substituted for the work experience requirements.

Additional Requirements:

• Knowledge, Skills, and Abilities (KSAs): Strategic Thinking; Advanced Quantitative Data Modeling; Algorithms; Applied Data Analysis; Data Extraction; Data Visualization Tools; Deep Learning/Neural Networks; Machine Learning; Relational Database Management; Project Management; Microsoft Excel; Design Thinking; Business Intelligence Tools; Data Manipulation/Wrangling; Data Ensemble Techniques; Feature Analysis/Engineering; Open Source Languages & Tools; Model Optimization; Data Architecture; Data Engineering

PDN-9ed9ec3a-3033-42e5-b87c-7f3672a96424