Top Senior Data Science Jobs in Salt Lake City, UT

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Design, develop, and deploy supervised machine learning models for credit decisioning. Collaborate with business and technical stakeholders to create data sources, improve modeling methodology, operationalize models, and manage model evaluation and risk for production systems.

Lead G2’s experimentation practice across products and digital marketplaces. Design experimentation standards, causal inference methods, metrics, guardrails, and analysis approaches; partner with product and engineering to implement experimentation platforms and instrumentation. Build models, design auctions, ship production systems, evaluate modern statistical methods, and address marketplace interference and low-traffic constraints. Mentor data scientists and evangelize rigorous data science practices across the organization.

Leads data science initiatives by analyzing complex datasets, developing and deploying machine learning and generative AI solutions, and applying RAG, fine-tuning, and agentic workflows. Improves healthcare outcomes, Star ratings, care gaps, payment integrity, and member experiences. Collaborates with technical and business teams, communicates analytical findings, documents methodologies, manages projects, and mentors data scientists.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

The Senior Data Scientist will analyze large-scale customer data, investigate behavioral questions, design and automate studies, and translate findings into actionable recommendations. The role includes maintaining analytics frameworks, documenting methodologies, writing white papers, and communicating insights across the company. Candidates should be independent, comfortable with ambiguity, and experienced with SQL and Redshift. Python or R, machine learning, Spark, A/B testing, and cross-functional experience are preferred.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Leads advanced analytics, machine learning, statistical modeling, and NLP solution development using healthcare and operational data. Builds scalable pipelines, visualizations, and deployed models on Google Cloud Platform, translates findings for clinical and executive leaders, designs experiments, and establishes performance metrics. Requires collaboration across clinical, technical, and data engineering teams, with occasional on-site travel for executive presentations.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Lead the development of production-grade machine learning solutions, collaborating with cross-functional teams to solve complex business problems and deliver measurable impact.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.