Data Scientist II Oakland, CA

Data Scientist II job at Kaiser Permanente. Oakland, CA.

The Entry‑Level Data Scientist will support Government Programs' Risk Adjustment team by applying foundational data science skills to analyze healthcare data and contribute to data‑driven solutions. This role offers the opportunity to work with Medicare Advantage, ACA, and Medicaid data, supporting efforts to improve risk score accuracy, ensure regulatory compliance, and enhance program performance. Working under the guidance of senior data scientists, this role involves using Python and cloud‑based analytics tools to clean, analyze, and visualize data from sources such as claims, encounter, and enrollment records.

The ideal candidate has a degree in data science or a related field, exposure to machine learning and statistical techniques, and a strong interest in applying data to real‑world healthcare challenges. This is a collaborative role with opportunities to learn from experienced team members while contributing to impactful projects that support data‑informed decision‑making across clinical, actuarial, and operational teams.

This individual contributor is primarily responsible for participating in the design and development of data pipelines, automation for data acquisition, and ingestion of raw data from multiple data sources and data formats under the guidance of more senior data scientists. This role is also responsible for assisting in the development of detailed problem statements outlining hypotheses and their effect on target clients/customers, analyzing and investigating data sets and summarizing key characteristics, selecting, manipulating and transforming data into features used in machine learning algorithms, training statistical models, assisting with the deployment and maintenance of reliable and efficient models through production, examining model performance, and working with internal and external stakeholders to develop and deliver statistically driven outcomes.

• Pursues effective relationships with others by sharing resources, information, and knowledge with coworkers and members.
• Listens to, addresses, and seeks performance feedback. Pursues self‑development; acknowledges strengths and weaknesses based on career goals and takes appropriate development action to leverage / improve them. Adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work. Assesses and responds to the needs of others to support a business outcome.
• Completes work assignments by applying up‑to‑date knowledge in subject area to meet deadlines; follows procedures and policies, and applies data and resources to support projects or initiatives with limited guidance and/or sponsorship. Collaborates with others to solve business problems; escalates issues or risks as appropriate; communicates progress and information. Supports the completion of priorities, deadlines, and expectations. Identifies and speaks up for ways to address improvement opportunities.
• Assists in the development of detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics under the guidance of more senior data scientists.
• Participates in the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats under the guidance of more senior data scientists by transforming, cleansing, and storing data for consumption by downstream processes; writing diverse SQL queries; and demonstrating a working knowledge of database fundamentals.
• Analyzes and investigates 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 under the guidance of more senior data scientists by leveraging techniques to conduct dimensionality reduction, feature importance, and feature selection.
• Trains…