Top Data Science Jobs in San Diego, CA

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

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.

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 the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

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 end-to-end data science and machine learning initiatives, including predictive modeling, statistical analysis, healthcare data extraction, and production deployment. Partners with business and technical leaders to develop data science roadmaps, establishes modeling and analytics standards, evaluates emerging technologies, and mentors data science professionals. The role requires strong programming, SQL, MLOps, stakeholder communication, and advanced analytics expertise.

Summer 2027 data science internship supporting Northrop Grumman’s Product Support organization. The intern will develop machine learning and predictive analytics solutions, build statistical tools, model complex data, write maintainable code, contribute to testing and documentation, implement CI/CD practices, and optimize algorithms and data workflows in an on-premise environment.

Performs data preparation, exploratory analysis, predictive modeling, forecasting, visualization, and analytical model validation. The role supports machine learning workflows, creates reports and dashboards, documents methodologies, and collaborates with project teams to address operational and business questions. The position requires an active Secret clearance, a bachelor’s degree, and at least three years of relevant experience, with preferred experience in data platforms, automation, visualization, and government or logistics operations.

Supports human performance analytics for Special Operations Forces by entering, cleaning, and analyzing data; conducting applied research and statistical analysis; developing dashboards and longitudinal readiness products; and communicating performance trends, risks, and program impacts to leadership. Collaborates with multidisciplinary human performance teams and government biostatisticians to inform operational readiness, resiliency, and return-to-duty outcomes.

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.

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.

Develop advanced analytical models, predictive forecasting solutions, AI/ML capabilities, dashboards, and visualization tools supporting Navy Reserve readiness and operational optimization. Apply statistical analysis, machine learning, data mining, and predictive modeling to identify risks and improve decisions. Collaborate with technical teams and government stakeholders, evaluate emerging analytics technologies, communicate findings, and potentially lead analytic workstreams and mentor junior staff.

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.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

Leads data science and advanced analytics initiatives for Auto Claims by developing machine learning and predictive modeling algorithms, integrating structured and unstructured data, and creating tools to support business decisions. Communicates modeling results to stakeholders, researches emerging techniques, manages moderately complex projects, and provides mentorship to junior data scientists. The role is remote with up to 10% travel to regional offices.

Develops data-driven solutions for sustainment and defense clients by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, and quantitative analyses using R or Python, machine learning, artificial intelligence, and natural language processing. The role applies data science to reliability, logistics, maintenance, and combat-system datasets and requires Secret clearance.

Designs, builds, evaluates, and deploys machine learning, LLM, and agentic AI capabilities for cybersecurity detection, investigation, and response. Analyzes complex security data, develops models and evaluation frameworks, creates RAG and retrieval workflows, and partners with engineering teams to productionize and monitor AI systems. The role requires addressing adversarial, noisy data and balancing accuracy, explainability, latency, reliability, and operational impact.

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.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

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.

Develop predictive models, algorithms, and data products from structured and unstructured data. Analyze datasets, validate models, conduct experiments, and use machine-learning methods to deliver actionable decision support. Collaborate with clients and subject-matter experts to assess data quality and identify analytic opportunities. Preferred work includes data pipelines, ETL, automated reporting, dashboards, distributed computing, simulation, optimization, and operations research.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.