Top Senior Data Science Jobs in Salt Lake City, UT

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Lead development of orthomosaic tile imagery products for FireSat and SBEM: design and implement scalable mosaicking, orthorectification, and radiometric normalization algorithms; write production-ready code; author technical and customer-facing documentation; validate scientific quality; engage stakeholders and act as product owner for SBEM granule product; collaborate with engineers and scientists.

Lead radiometric calibration and image processing for multispectral infrared instruments, commission FireSat satellites, and develop/execute SBEM calibration plans. Implement production-ready pipelines, perform R&D to improve calibration, write production-quality code, and collaborate cross-functionally to maintain operational remote sensing data products and services.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

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 healthcare and pharmaceutical marketing analytics: analyze CRM, media, healthcare and behavioral data; build segmentation, attribution, audience scoring, and predictive models; develop measurement frameworks; use Python, SQL, BI and automation; translate insights for executives; mentor juniors and partner with Strategy, Media, and Client Services.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

Lead applied AI projects end-to-end: prototype, validate, and help deploy ML and GenAI solutions (malicious behavior, anomaly/fraud detection, developer experiences). Act as internal AI consultant, design experiments and evaluations, bridge research and production, mentor peers, and ensure data governance and privacy.

Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.

Lead design and deployment of ML systems for enterprise search, retrieval, recommendations, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and KPIs. Apply statistics, causal inference, experimentation, and analysis to guide product strategy. Prototype and productionize LLM-based features, mentor junior staff, and collaborate cross-functionally.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

Lead data-driven strategy by partnering with senior executives, design and deliver sophisticated analytical solutions on large-scale data, build trusted cross-functional relationships, and mentor data scientists to raise analytical quality across the organization.

Lead data-driven strategy by partnering with senior executives to deliver rigorous analytical solutions. Design and implement sophisticated analyses on large-scale data, build trusted cross-functional relationships, mentor data scientists, and influence long-term product and business decisions.

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.

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

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Design, build, and deploy predictive GTM models (LTV, attribution, propensity). Lead experimentation and causal analyses, define KPI measurement frameworks, partner with Data Engineering, and translate statistical insights into actionable recommendations for Sales, Marketing, and Customer Success.

Lead a 5–8 person Data Science team to develop and operationalize FWA detection models using SAS and Python on multi‑payer healthcare claims. Oversee model development, data pipelines, quality reviews, stakeholder presentations, and collaboration with SMEs and BI developers. Split role ~40% management, 60% hands-on coding/analysis.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.