Top Senior Data Science Jobs in Sacramento, CA

The Data Scientist II will drive insights, develop models, and analyze product experiments to inform business decisions and improve member experiences in healthcare.

Designs, analyzes, and interprets clinical trial, RWE, and observational data for a medical device. Develops statistical plans, predictive models, and ensures data quality, regulatory compliance, and traceability. Collaborates with clinical, regulatory, and data teams, supports submissions and publications, mentors interns, and represents the company externally.

Design, validate, and deploy predictive models using claims, clinical, pharmacy, and lab data to improve population health, utilization management, and care quality. Partner with clinical and business teams, evaluate model performance, scale analytical approaches, and communicate insights to technical and non-technical stakeholders.

Lead the intelligence layer atop the data lakehouse: design and run causal experimentation, build predictive and forecasting models, own model evaluation and monitoring, deploy and maintain models on Databricks/MLflow, set technical direction for data science, and partner closely with data engineers, AI engineers, and product. Use AI development tools daily.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Partner with business, finance, and engineering teams to design and evaluate incentive compensation plans. Build scalable data pipelines, ML models, NLP and AI agents, and dashboards to generate actionable insights, perform scenario modeling, and ensure quota and compensation outcomes are explainable, reliable, and aligned to business goals.

Lead development and deployment of ML and AI solutions for global incentive and quota design. Partner cross-functionally to define quota strategy, build scalable data pipelines and models, create AI agents, evaluate model performance, ensure governance/compliance, and communicate insights to stakeholders to drive business outcomes.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

Develop and validate SDTM and ADaM datasets using R following CDISC standards, support TLF generation in R/SAS, perform QC and reconcile data issues, collaborate with statisticians and clinical teams, document workflows, and support automation of R-based pipelines for clinical trial deliverables.

Lead customer-facing data science engagements: manage data acquisition/cleaning, develop and evaluate scalable models, drive modeling best practices, contribute thought leadership, write extensible code, and define solution and business strategy with cross-functional teams.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead customer-facing data science engagements: acquire and prepare data, evaluate and operationalize models at scale, drive modeling best practices, research industry trends, write extensible code, and define solution and customer strategy while promoting ethics and privacy.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Lead development and deployment of scalable ML, prescriptive analytics, and constrained-optimization models for real-time routing, scheduling, capacity allocation, and personalization in cloud gaming. Build data ingestion and processing frameworks, improve data quality, analyze patterns with supervised/unsupervised methods, and collaborate cross-functionally to deliver prescriptive pipelines and actionable recommendations that optimize latency, utilization, and retention.

Lead development and deployment of machine learning models, perform EDA and data engineering on large datasets, train models including LLMs and unstructured-data solutions, build Power BI dashboards, automate processes, present findings to leadership, and mentor junior data scientists while working with SQL, Python, AWS/Azure, Snowflake, and Salesforce.

Design, implement, and maintain marketing inference models (e.g., MMM and attribution). Analyze large datasets and A/B/uplift experiments with rigorous statistics, build predictive and causal models, partner with marketing to generate testable ideas, and communicate insights to drive marketing decisions.

Lead end-to-end delivery of strategic data science and AI projects: define business problems with stakeholders, prepare large datasets, build and deploy predictive, prescriptive, and generative AI models (including LLMs), implement prompt engineering and fine-tuning, ensure production-grade code and ML lifecycle practices, present insights to senior stakeholders, and promote responsible AI and adoption across teams.

Lead product and program analytics for Microsoft skilling experiences. Design funnel and learner-journey analyses, define success metrics, build measurement frameworks, run experiments and causal analyses, develop competency/skill models, partner with product and engineering on telemetry, recommend content and curriculum improvements, mentor data scientists, and ensure data privacy and telemetry quality.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

As a Senior Marketing Data Scientist, you will conduct quantitative analyses, manage marketing tests, and provide actionable insights to enhance marketing performance and investment effectiveness.

Lead cross-functional teams to identify and implement revenue-driving strategies. Own revenue optimization, develop quantitative analyses (A/B testing, causal impact), improve data pipelines and tracking, embed AI into workflows, and present insights to leadership to scale and recover revenue.

Seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives. Responsibilities include building ML models, data management, and mentoring junior data scientists. Must have expertise in AWS and strong statistical knowledge.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.