Top Hybrid Data Science Jobs

Leads finance transformation, data science, analytics, forecasting, anomaly detection, automation, and AI initiatives. Partners with finance, technology, and business leaders to improve reporting, financial data quality, forecasting, and decision-making. Develops data science roadmaps, deploys production models, establishes governance and responsible AI standards, measures business impact, and presents findings to executives. Coaches a data science team and manages cross-functional priorities.

Leads UCI schema analysis, integration, compliance, and lifecycle management for Air Force mission-level Command and Control systems. Designs interoperable data flows, improves data quality, supports testing and validation, documents system changes, and collaborates with technical and operational teams. The role also applies data analytics, machine learning, and statistical methods within secure defense environments.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Leads data scientists and machine learning engineers developing personalized customer experiences. Sets strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; oversees machine learning systems from design through deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability practices; and manages capacity, resources, technical standards, and team development.

Build statistical and machine learning models that improve content discovery, user engagement, and retention. Apply causal inference, experimental design, and A/B testing to measure product impact and validate changes. Collaborate with ML engineers to deploy models, translate analysis into product decisions, and develop features using text, multimodal embeddings, and GenAI. Analyze large-scale data and recommender systems to drive measurable improvements in a consumer product serving millions of users.

Develops clinically meaningful data science and healthcare informatics solutions for medication safety and drug diversion surveillance. The role profiles, maps, normalizes, and validates healthcare data; supports customer configuration, onboarding, investigations, and post-live troubleshooting; builds synthetic datasets; collaborates on ETL pipelines and product features; and communicates findings through validation reports, documentation, and stakeholder updates.

Develops data science, AI, machine learning, NLP, statistical, and visualization solutions for government audit and investigative work. Analyzes structured and unstructured data to identify fraud, waste, and abuse; builds and maintains models and analytical products; uses cloud data environments and relational databases; documents methodologies and technical requirements; communicates findings to stakeholders; and supports training, project collaboration, and agency mission objectives.

Leads the strategic vision, organizational direction, and delivery of advanced data science programs. Manages multiple teams of data scientists and analysts, including managers, while overseeing statistical modeling, machine learning, data pipelines, and analytical frameworks. Partners with executive stakeholders to align initiatives with enterprise goals, advocates for resources, develops talent, ensures regulatory compliance, and promotes innovation and ethical data practices.

Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.

Lead data science research and production development for attribution, identity resolution, tracking quality, and incrementality measurement. Design experiments, build and monitor production pipelines, evaluate third-party identity vendors, and develop ML and graph-based solutions. Translate technical findings into recommendations for Product, Engineering, and leadership while shaping measurement strategy and build-versus-buy decisions.