Data Scientist IV - Risk Adjustment%5C: Medicare, Medicaid, ACA

Kaiser Permanente; Renton (WA); On-site; USD 140,000 - 200,000; Full time

Job summary

Kaiser Permanente in Renton, WA, seeks a Data Scientist IV to design and implement scalable data pipelines and machine learning models, advancing risk adjustment across Medicare Advantage, ACA, and Medicaid programs.

You will work with clinical, actuarial, and operational teams to translate complex analytics into actionable insights, driving regulatory compliance and financial performance while mentoring junior data scientists and fostering a data-driven culture.

Qualifications

• Minimum three years of experience in Exploratory Data Analysis (EDA) and visualization.
• Minimum three years of machine learning and/or algorithmic experience.
• Minimum three years of statistical analysis and modeling experience.
• Minimum three years of programming experience.
• Minimum one year of experience in a leadership role, with or without direct reports.
• Bachelor's degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field plus five years of data science experience; advanced degrees may substitute requirements.

Responsibilities

• Designs and develops data pipelines and automation for data acquisition and ingestion from multiple sources.
• Transforms, cleanses, and stores data for downstream use.
• Develops hypotheses, defines scope, objectives, and metrics for outcomes.
• Transforms data into features for machine learning models and performs feature selection.
• Trains, validates, and deploys statistical models in production and maintains performance.
• Collaborates with stakeholders to translate analyses into actionable insights for technical and non-technical audiences.

Skills

EDA & Visualization

ML/Algorithms

Statistical Analysis

Programming

Leadership

Education

Bachelor's degree + 5+ years data science

Job description

** PLEASE NOTE: Salary ranges are geographically based and the posted range reflects the Northen CA region. Lower salary ranges will apply for other labor markets outside of NCAL

Overview:

The Risk Adjustment Strategic Analytics team is seeking a Data Scientist IV to play a strategic and technical role in advancing Government Programs’ Risk Adjustment 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 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 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 individual contributor is primarily responsible for designing and developing data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption. This role is also responsible for developing detailed problem statements outlining hypotheses and their effect on target clients/customers, analyzing and investigating complex data sets and summarizing key characteristics, selecting, manipulating and transforming data into features used in machine learning algorithms, training statistical models, deploying and maintaining reliable and efficient models through production, verifying model performance, and collaborating with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Essential Responsibilities:

• Promotes learning in others by proactively providing and/or developing information, resources, advice, and expertise with coworkers and members; builds relationships with cross-functional/external stakeholders and customers. Listens to, seeks, and addresses performance feedback; proactively provides actionable feedback to others and to managers. Pursues self-development; creates and executes plans to capitalize on strengths and develop weaknesses; leads by influencing others through technical explanations and examples and provides options and recommendations. Adopts new responsibilities; adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work; champions change and helps others adapt to new tasks and processes. Facilitates team collaboration to support a business outcome.
• Completes work assignments autonomously and supports business-specific projects by applying expertise in subject area and business knowledge to generate creative solutions; encourages team members to adapt to and follow all procedures and policies. Collaborates cross-functionally and/or externally to achieve effective business decisions; provides recommendations and solves complex problems; escalates high-priority issues or risks, as appropriate; monitors progress and results. Supports the development of work plans to meet business priorities and deadlines; identifies resources to accomplish priorities and deadlines. Identifies, speaks up, and capitalizes on improvement opportunities across teams; uses influence to guide others and engages stakeholders to achieve appropriate solutions.
• Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics.
• Designs and develops data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats by transforming, cleansing, and storing data for consumption by downstream processes; writing and optimizing diverse SQL queries; and demonstrating advanced knowledge of database fundamentals.
• Analyzes and investigates complex data sets and summarizes key characteristics by employing data visualization methods; and determining how best to manipulate data sources to discover patterns, spot anomalies, test hypotheses, and/or check assumptions.
• Selects, manipulates, and transforms data into features used in machine learning algorithms by leveraging techniques to conduct dimensionality reduction, feature importance, and feature selection.
• Trains statistical models by using algorithms and data mining techniques; testing models with various algorithms to assess the input dataset and related features; and applying techniques to prevent overfitting such as cross-validation.
• Deploys and maintains reliable and efficient models through production.
• Verifies model performance by demonstrating 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.
• Collaborates with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by 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 audiences.

Minimum Qualifications:

• Minimum three (3) years experience working with Exploratory Data Analysis (EDA) and visualization methods.
• Minimum three (3) years machine learning and/or algorithmic experience.
• Minimum three (3) years statistical analysis and modeling experience.
• Minimum three (3) years programming experience.
• Minimum one (1) year 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 five (5) 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): Advanced Quantitative Data Modeling; Algorithms; Applied Data Analysis; Data Extraction; Data Visualization Tools; Machine Learning; Relational Database Management; Microsoft Excel; Design Thinking; Business Intelligence Tools; Data Manipulation/Wrangling; Data Ensemble Techniques; Feature Analysis/Engineering; Open Source Languages & Tools; Model Optimization; Strategic Thinking; Deep Learning/Neural Networks; Project Management