Data Scientist III

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

Overview:
Make Data Matter. Change Lives at Scale.
As a Data Scientist in at Kaiser Permanente, you will use data, analytics, and scientific thinking to improve the health and well-being of millions of people. This role sits at the intersection of advanced analytics, real-world healthcare problems, and human impact, where your work directly supports better care, smarter operations, and more equitable outcomes.
You will tackle some of the most challenging and meaningful data problems in healthcare, collaborating with an exceptional community of analysts, data scientists, data engineers, clinicians, and operational leaders who are united by a shared mission: helping people live healthier lives.
The Data Scientist is an experienced individual contributor using SQL, Python and sometimes the latest Generative AI capabilities to develop insights at patient and encounter level, identifying risks, actions, and workflow integrations to seamlessly integrate our insights and to achieve data-driven impact.

Job Summary:
This individual contributor is primarily responsible for participating 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. This role is also responsible for developing 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 under the guidance of more senior data scientists, deploying and maintaining reliable and efficient models through production, verifying model performance, and working with internal and external stakeholders across domains to develop and deliver statistical driven outcomes.

Essential Responsibilities:

• Pursues effective relationships with others by proactively providing resources, information, advice, and expertise with coworkers and members. Listens to, seeks, and addresses performance feedback; provides mentoring to team members. Pursues self-development; creates plans and takes action to capitalize on strengths and develop weaknesses; influences others through technical explanations and examples. Adapts to and learns from change, challenges, and feedback; demonstrates flexibility in approaches to work; helps others adapt to new tasks and processes. Supports and responds to the needs of others to support a business outcome.
• Completes work assignments autonomously by applying up-to-date expertise in subject area to generate creative solutions; ensures all procedures and policies are followed; leverages an understanding of data and resources to support projects or initiatives. Collaborates cross-functionally to solve business problems; escalates issues or risks as appropriate; communicates progress and information. Supports, identifies, and monitors priorities, deadlines, and expectations. Identifies, speaks up, and implements ways to address improvement opportunities for team.
• Develops detailed problem statements outlining hypotheses and their effect on target clients/customers by defining scope, objectives, outcome statements and metrics.
• 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 and optimizing 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 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.
• Deploys and maintains reliable and efficient models through production.
• Verifies model performance by demonstrating a working knowledge 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.
• Works with internal and external stakeholders across domains to develop and deliver statistical driven outcomes by delivering insights and values from heterogeneous data to investigate problems for multiple use cases; driving informed decision-making; and presenting findings to both technical and non-technical audiences.

Knowledge, Skills and Abilities: (Core)

• Ambiguity/Uncertainty Management
• Attention to Detail
• Business Knowledge
• Communication
• Critical Thinking
• Cross-Group Collaboration
• Decision Making
• Dependability
• Diversity, Equity, and Inclusion Support
• Drives Results
• Facilitation Skills
• Health Care Industry
• Influencing Others
• Integrity
• Learning Agility
• Organizational Savvy
• Problem Solving
• Short- and Long-term Learning & Recall
• Teamwork
• Topic-Specific Communication

Knowledge, Skills and Abilities: (Functional)

• Advanced Quantitative Data Modeling
• Algorithms
• Applied Data Analysis
• Business Intelligence Tools
• Data Ensemble Techniques
• Data Extraction
• Data Manipulation/Wrangling
• Data Visualization Tools
• Design Thinking
• Feature Analysis/Engineering
• Machine Learning
• Microsoft Excel
• Model Optimization
• Open Source Languages & Tools
• Relational Database Management

Minimum Qualifications:

• Minimum two (2) years experience working with Exploratory Data Analysis (EDA) and visualization methods.
• Minimum one (1) year machine learning and/or algorithmic experience.
• Minimum two (2) years statistical analysis and modeling experience.
• Minimum two (2) years programming experience.
• Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field AND Minimum three (3) 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.

Preferred Qualifications:

• One (1) year experience in a leadership role with or without direct reports.
• One (1) year healthcare experience.

Primary Location: California,Pleasanton,Pleasanton Tech Cntr Building A Additional Locations:

• Pasadena
• Honolulu
• Atlanta
• Rockville
• Aurora
• San Diego
• Portland
• Washington
• Alexandria
• Renton

Scheduled Weekly Hours: 40
Shift: Day
Workdays: Mon, Tue, Wed, Thu, Fri
Working Hours Start: 08:30 AM
Working Hours End: 05:00 PM
Job Schedule: Full-time
Job Type: Standard
Worker Location: Flexible
Employee Status: Regular
Employee Group/Union Affiliation: NUE-PO-01|NUE|Non Union Employee
Job Level: Individual Contributor
Department: Po/Ho Corp - KP Insight HQAA - 0308
Pay Range: $146600 - $189640 / year Kaiser Permanente is committed to pay equity and transparency. The posted pay range is based on possible base salaries for the role and does not include the value of our total rewards package. Actual pay determined at offer will be based on years of relevant work experience, education, certifications, skills and geographic location along with a review of current employees in similar roles to ensure that pay equity is achieved and maintained across Kaiser Permanente.
Travel: No
Flexible: Work location is on-site at a KP location, with the flexibility to work from home. Worker location must align with Kaiser Permanente's Authorized States policy. Kaiser Permanente is an equal opportunity employer committed to fair, respectful, and inclusive workplaces. Applicants will be considered for employment without regard to race, religion, sex, age, national origin, disability, veteran status, or any other protected characteristic or status.