Top Remote Data Science Jobs in Salt Lake City, UT

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

As a Data Scientist III, you'll build customer identity systems, develop recommendation models, predictive analytics for customer behavior, and collaborate with ML Ops and Data Engineering.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Build, optimize, and scale production Generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and deploy LLMs, and create high-performance FastAPI async backends. Improve latency, cost, and reliability, containerize services with Docker, and manage monitoring, API security, and CI/CD for ML deployments.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Conduct data science research on experimental methodologies and causal modeling, develop tools to analyze experimental data, prototype improvements to platform features, and communicate results to internal teams and external stakeholders to drive product and research decisions.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

Design, build, and deploy search and recommendation ML systems (ranking, retrieval, personalization, query understanding) using embeddings and LLM approaches. Collaborate with engineers and product to create scalable data pipelines, integrate models via APIs, run A/B tests, and monitor online/offline metrics to improve relevance and engagement.

Lead data science across Growth and Membership Operations: define problems, build scalable data models and pipelines, develop analytical and ML solutions, partner cross-functionally, and integrate AI tools to optimize sales, marketing, telesales, and enrollment operations.

As a Senior Data Scientist, you will develop machine learning models, optimize deployment processes, and collaborate with cross-functional teams to enhance clinical AI workflows.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Data Scientist II will develop machine learning algorithms, architect data solutions, and support cybersecurity initiatives by analyzing complex data sets.