Top Data Science Jobs in San Diego, CA

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Leads enterprise-scale data science and statistical modeling projects supporting government evaluations, ROI analysis, risk factors, and program effectiveness. Reviews analytical designs, code, and results; mentors data scientists; establishes reproducible methods and validation standards; develops dashboards, scorecards, KPIs, and performance measures; and advises government stakeholders on model interpretation and limitations.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Build and deploy NLP and machine learning models that analyze student and educator text. Develop classical ML and LLM-based features, production inference systems, validation and monitoring pipelines, data-drift detection, retraining workflows, and responsible AI guardrails. Partner with product, engineering, and data science teams to improve real-time education technology features while ensuring accuracy, fairness, privacy, and reliability.

Leads fraud detection, risk analysis, loss mitigation, and fraud strategy development. Builds analytical rules, predictive algorithms, monitoring dashboards, and automated controls using SQL, Python, Tableau, and AI tools. Partners with product, engineering, modeling, and operations teams to define requirements, validate strategies, optimize fraud KPIs, and manage deployments. The role also involves experimental design, governance, onboarding fraud typologies, and mentoring junior team members.

Applies statistical analysis, data processing, visualization, and modeling to complex structured and unstructured datasets. Develops data solutions using Python, R, SQL, and related technologies; creates dashboards and reports; maintains data integrity and governance; and collaborates with stakeholders on business or mission objectives. The role also requires cloud-native application deployment using Kubernetes, Docker or Podman, Helm, Git workflows, Linux, configuration management, and software release processes, with preferred experience in microservices, APIs, event-driven systems, PostgreSQL, Redis, and cloud-native architectures.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Design and analyze rigorous messaging experiments using A/B testing and causal inference. Build measurement frameworks, data pipelines, dashboards, automated analyses, and scalable data solutions for messaging campaigns. Partner with CRM, Product, Algorithms, and Engineering teams to translate ambiguous business questions into actionable recommendations and experimentation standards. Support messaging strategy across channels, timing, content, targeting, and new business verticals.

Develop and productionize machine learning models and predictive analytics using Python, Databricks, PySpark, SQL, and Delta Lake. Build scalable data science workflows, resolve healthcare data quality and entity-resolution issues, and apply Master Data Management practices. Partner with engineering, product, analytics, and business teams to deliver governed, HIPAA-compliant solutions. Perform exploratory analysis, feature engineering, model evaluation, deployment, documentation, and data lineage management for healthcare and member engagement initiatives.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

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.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Lead product and program analytics for Microsoft skilling experiences: design learner journey/funnel analyses, define success metrics, run experiments and causal analyses, build proficiency/competency models, shape telemetry and scalable reporting, recommend content and curriculum improvements, and mentor other data scientists to drive product decisions and learning outcomes.