Top Remote Data Science Jobs in Sacramento, CA

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Work with clients and engineers to audit marketing datasets, build analytics pipelines, and apply predictive models (segmentation, attribution, propensity) using SQL, Python, cloud big-data tools, and ML frameworks. Support implementations, develop analytic applications, and communicate findings to drive marketing outcomes.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

The Data Scientist will analyze user behavior, create segmentation strategies, design experiments, and track business metrics to drive growth in a SaaS company.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

The Data Scientist 1 role involves cleaning and analyzing complex datasets, developing statistical models, and communicating insights through data visualizations. Responsibilities include collaborating with teams, maintaining data quality, and keeping up with advancements in the field.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Lead end-to-end data science projects, develop machine learning models, collaborate with stakeholders, mentor team members, and communicate insights effectively.

As a Senior Data Scientist, you will collaborate with cross-functional teams to drive AI/ML product development, analyze user data, and influence product strategies.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Lead development and deployment of predictive models, analytics, and dashboards to support Sales, Marketing, Customer Success, and GTM operations. Mentor data scientists, partner with business leaders to prioritize analytics, ensure data governance and quality, and present insights to executives. Work with Snowflake/data lakes, build scalable ML solutions, and drive analytics adoption across the enterprise.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Apply machine learning to large datasets to build predictive models, perform data mining, and develop algorithms. Analyze results, create visualizations, and provide insights to inform business decisions.

The Applied Data Scientist will manage client engagements, unifying datasets, building models, deriving insights, and collaborating with teams to implement solutions.

The Data Scientist I will analyze large datasets, build predictive models, conduct A/B testing, and communicate insights to stakeholders, enhancing business solutions.

The Data Scientist will develop and improve models and LLM systems for security applications, focusing on production readiness and experimentation. Responsibilities include building classifiers and evaluating models.

Build and productionize machine learning models for healthcare products, define and track KPIs, collaborate with product and customers to scope analytics projects, communicate results to technical and non-technical stakeholders, and improve analytics practices across the team.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

As a Compliance Data Scientist Co-op, you will automate data processing, enhance Power BI reports, and utilize AI for compliance improvements.

As a Staff Data Scientist, you will create and integrate AI/ML models into performance management, conducting data analysis, statistical modeling, and collaborating with engineering teams for operationalization.