Top Senior Data Science Jobs in San Diego, CA

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Develops advanced analytics, dashboards, data-entry solutions, and automated tools to support operational strategy and execution. Facilitates performance reviews, improves data quality, coaches stakeholders on data literacy, translates analytics into actionable insights, and leads cross-functional continuous-improvement initiatives across manufacturing and operational teams.

Develops algorithms, predictive models, and data systems to analyze complex structured and unstructured datasets. Partners with clients to define analytical needs, performs data exploration, cleaning, mining, visualization, and modeling, and translates findings into actionable insights. The role uses statistical programming, machine learning, AI, and distributed computing tools to support decision-making across public and private sector challenges.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Leads the Enterprise Data and Analytics function, directing data science and engineering teams while advising senior government leaders. Oversees research methodologies, study design, program evaluations, statistical modeling, data governance, dashboards, and decision-support products. Establishes analytical standards, ensures compliance with government and healthcare data policies, coordinates with enterprise data organizations, performs advanced analyses, and supports staff development.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Lead a small data science team while remaining hands-on in building advertising audience, measurement, ranking, and data-feed models. Set technical direction, apply causal inference and experimental design, build scalable production systems, mentor scientists, and partner with product, engineering, sales, and business development teams. Translate campaign data into trusted measurement products, influence senior leaders and customers, and establish Fetch’s methodology as a market standard.

Leads enterprise data and analytics, advising government leadership on research methods, study design, program evaluation, and decision support. Oversees data scientists and engineers, establishes analytical and governance standards, directs advanced statistical and predictive analyses, develops dashboards and readiness metrics, and ensures reproducible, compliant methodologies. Requires strong Python or R, SQL, supervisory, research, and stakeholder communication skills, plus a current Top Secret clearance and ability to obtain Top Secret/SCI clearance.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.