Top Data Science Jobs in San Diego, CA

Lead analytics and measurement for a Sales subdomain: monitor KPIs, investigate performance issues, design experiments, build data foundations and self-serve dashboards, use agentic AI to automate workflows, and communicate results to senior sales, finance, and operations stakeholders.

Lead analytics for a Sales subdomain: measure performance, investigate drivers, design experiments, and deliver end-to-end data science solutions. Build and maintain data foundations, self-serve dashboards, and communicate results to senior sales, finance, and operations stakeholders. Use agentic AI and modern GTM tooling to scale impact and improve decision-making.

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.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

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.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

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.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

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.

Lead design, build, and production deployment of generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs/services and retrieval pipelines, optimize performance, ensure responsible AI, and mentor team members while setting AI technical direction across the organization.

As a mid-level data scientist, you will analyze data, develop algorithms, and utilize machine learning to provide insights and solutions for various sectors, including fraud detection and healthcare. You'll work with clients to understand their needs and turn data into actionable information.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead analytics effort to collect, clean, integrate, analyze data and build/monitor predictive models (including recommendation systems). Partner with stakeholders, mentor data scientists, perform EDA, root cause analysis, and consult on analytics-driven strategies.

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.

Perform large-scale data analysis and modeling using Hadoop ecosystems and general-purpose languages (Java, Python, R). Apply statistical techniques and machine learning on big datasets, develop creative solutions, visualize patterns, and work independently within a research-oriented team. Strong communication and rapid conceptual learning are expected.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

The Data Scientist role involves analyzing payment and subscription experiences, applying data science methods, and collaborating with cross-functional teams to improve player experiences and business decisions.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

Lead end-to-end supply chain planning and forecasting modeling: demand forecasting across geographies, forecasting tournaments, inventory placement optimization, vendor performance modeling, and raw-material signal generation. Collaborate with leadership and engineering to productionize models and apply AI-native techniques (LLM-aided EDA, agentic feature engineering, AI-augmented experimentation).

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Develop, analyze, and deploy data-driven and ML/AI solutions. Perform data analysis, visualization, and decision-support work using Python and version control. Support operational deployments, containerized environments, and cybersecurity considerations; apply NLP/LLM techniques when relevant.

Lead quantitative analysis and modeling of large real-world health datasets to estimate population-level disease prevalence and incidence. Design and implement statistical and machine learning models, maintain data pipelines and dashboards, ensure data quality and reproducibility, collaborate with clinicians and public health experts, prepare scientific reports, and support team onboarding and training.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.