Top Hybrid Data Science Jobs

Develop machine learning models and advanced analytics solutions across the full model lifecycle, including data exploration, feature engineering, validation, refinement, and operational monitoring. Build AI capabilities for intelligent products and data-driven decisions while monitoring system health, user metrics, performance, and availability. Collaborate across teams, mentor technical staff, optimize processes, and apply secure software development practices.

Leads the technical vision and scientific strategy for AbbVie’s omnichannel engagement analytics. Develops predictive, causal, recommendation, sequential decision, and generative AI solutions; establishes rigorous measurement and experimentation standards; and guides production deployment, monitoring, and lifecycle management. Partners with commercial, regulatory, technology, product, and vendor stakeholders, influences executives, mentors data scientists, and advances responsible, reproducible AI/ML practices.

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.

Own end-to-end applied research on business ontologies, knowledge graphs, semantic layers, and AI agents over enterprise data. Develop methods using schema matching, entity resolution, LLMs, graph neural networks, and relational models; build benchmarks, evaluation systems, and prototypes adopted by product teams. Collaborate with universities, publish research or open-source work, communicate findings clearly, and mentor interns. The hybrid Palo Alto role requires up to 10% travel.

Leads end-to-end data science solutions for clinical development and trial operations. Applies machine learning, GenAI, optimization, simulations, and real-world data to study feasibility, enrollment forecasting, site selection, protocol design, evidence generation, and operational planning. Develops LLM workflows, validates and deploys models, ensures responsible AI and MLOps practices, translates findings for stakeholders, drives adoption, and mentors junior colleagues.

Lead data science and machine learning initiatives supporting small business lending and commercial credit risk. Develop predictive models, advanced algorithms, and automated analytical solutions for underwriting, pricing, portfolio management, and lending decisions. Translate complex datasets into actionable insights, advise senior leaders, lead projects, promote ethical AI, mentor team members, and advance analytical practices across the organization.

Analyze large datasets to identify fraud patterns, emerging risks, and strategy improvements. Develop fraud detection rules and models, monitor performance and loss metrics, build dashboards and reporting, evaluate data sources and analytical methods, and translate findings into recommendations. The role partners with Fraud, Risk, Product, Technology, and Operations teams while applying statistical analysis, predictive modeling, AI tools, and sound judgment when handling sensitive data.

Supports strategic pricing through advanced analytics, pricing models, competitive intelligence, elasticity and profitability analysis. Builds Power BI and Tableau dashboards, automated reporting, KPI scorecards, and analytical frameworks. Maintains pricing systems, performs UAT, documents processes, improves workflows, and supports cross-functional analytics projects. Translates complex datasets into recommendations for pricing, sales, marketing, product, finance, IT, and leadership stakeholders.

Build and deploy predictive models for automotive lending, including lender approval and rate predictions. Own data pipelines from dealer systems, CRMs, credit bureaus, and providers into Redshift, maintain lender rules and rate tables, monitor model performance, and develop data-driven product features. Collaborate with sales, customer success, dealerships, and engineering while identifying business problems, prioritizing solutions, and shipping prototypes, analyses, dashboards, and full-stack features using AI-assisted development.

The Senior Data Scientist develops quantitative models for financial crime compliance, performs data analysis, and collaborates across teams to enhance model performance.