Job Description

Overview: 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. Job Summary: 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 under the guidance of more senior data scientists, training statistical models under the guidance of more senior data scientists, assisting with the deployment and maintenance of reliable and efficient models through production, examining model performance, and working with internal and external stakeholders under the guidance of more senior data scientists to develop and deliver statistical driven outcomes. Essential Responsibilities: + 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 statistical models under the guidance of more senior data scientists 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. + Assists with the deployment and maintenance of reliable and efficient models through production. + Examines model performance by demonstrating a working knowledge of a variety of model validation techniques to assess and discriminate the goodness of model fit; and identifying feedback and output to inform and strengthen model performance. + Works with internal and external stakeholders under the guidance of more senior data scientists to develop and deliver statistical driven outcomes by providing insights and values from heterogeneous data to investigate problems for use cases; and supporting informed decision-making. Minimum Qualifications: + Minimum one (1) year statistical analysis and modeling experience. + Minimum one (1) year programming experience. + Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field OR Minimum two (2) years experience in data science or a directly related field. Advanced degrees may be substituted for the work experience requirements. Additional Requirements: + Knowledge, Skills, and Abilities (KSAs): Advanced Quantitative Data Modeling; Applied Data Analysis; Data Extraction; Data Visualization Tools; Relational Database Management; Microsoft Excel; Design Thinking; Business Intelligence Tools; Data Manipulation/Wrangling; Open Source Languages & Tools; Model Optimization; Algorithms; Machine Learning; Data Ensemble Techniques; Feature Analysis/Engineering COMPANY: KAISER TITLE: Data Scientist II LOCATION: Oakland, California REQNUMBER: 1352749 External hires must pass a background check/drug screen. Qualified applicants with arrest and/or conviction records will be considered for employment in a manner consistent with Federal, state and local laws, including but not limited to the San Francisco Fair Chance Ordinance. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, protected veteran, or disability status.

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