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.

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
• Deep Learning/Neural Networks
• Design Thinking
• Feature Analysis/Engineering
• Machine Learning
• Microsoft Excel
• Model Optimization
• Open Source Languages & Tools
• Project Management
• Relational Database Management

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.

Preferred Qualifications:

• One (1) year experience working with Kubernetes.
• One (1) year experience working with Docker.
• Three (3) years data wrangling experience.
• Master's degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field.
• Three (3) years experience working with SQL.
• Three (3) years experience working with Open Source languages (e g , R, Python, Scala).
• One (1) year deep learning experience.
• One (1) year data simulation experience.
• One (1) year healthcare experience.
• One (1) year experience working with Power BI.

Primary Location: California,Pleasanton,Pleasanton Tech Cntr Building B
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-NCAL-09|NUE|Non Union Employee
Job Level: Individual Contributor
Department: Oakland Reg - 2000 Broadway - RI Medical Records_Recharge - 0201
Pay Range: $169800 - $219670 / year Kaiser Permanente strives to offer a market competitive total rewards package and is committed to pay equity and transparency. The posted pay range is based on possible base salaries for the role and does not reflect the full value of our total rewards package. Actual base pay determined at offer will be based on labor market data, internal alignment, and a candidate's years of relevant work experience, education, certifications, skills, and geographic location.
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.