Top Hybrid Data Science Jobs

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Leads data science for consumer and developer experiences, partnering with engineers and product managers to deliver customer-focused products. Builds, evaluates, validates, and deploys machine learning models using large-scale numerical and textual data. Applies statistical modeling, A/B testing, clustering, classification, sentiment analysis, time series, and deep learning while translating technical insights into business outcomes. The role also includes team leadership, talent development, and evaluating emerging AI and cloud technologies.

Build and operate secure, scalable AI/ML platforms across on-premises, hybrid, and cloud environments. Responsibilities include data pipelines, MLOps workflows, model deployment, Kubernetes platform engineering, CI/CD and GitOps automation, observability, infrastructure as code, DevSecOps, data platform integration, incident response, and reliability improvement. The role supports traditional machine learning and generative AI applications, contributes to architecture and platform standards, and may provide technical leadership and mentorship.

Leads AI/ML and data science products that improve engineering productivity. Translates ambiguous business challenges into scalable, production-grade solutions, shapes scientific direction, ensures methodological quality, mentors scientists, develops reusable frameworks, manages risks and delivery, and collaborates across engineering, data, software, and business teams. Communicates technical tradeoffs to stakeholders and leaders while driving measurable enterprise impact.

Supports data scientists with data exploration, cleaning, analysis, model development, validation, visualization, and prototypes. Uses Python and SQL while learning machine learning, generative AI, cloud data platforms, and production data science practices. Participates in reviews, documentation, presentations, and collaborative projects across multiple co-op terms, progressively increasing responsibility under mentorship.

Leads international data science and analytics product development, managing multiple teams of analytics managers and professionals. Oversees predictive modeling, analytical product delivery, talent development, budgeting, capacity planning, resource allocation, project management, reporting, operational policies, and stakeholder alignment. Requires management of 20+ employees, advanced analytics expertise, customer problem framing, global market knowledge, and formal presentation skills. International travel up to 50% is required.

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

Lead experimentation, product analytics, and reporting for Owners and Sellers. Conduct exploratory, descriptive, statistical, and predictive analyses; design multivariate tests; define success metrics; and translate consumer behavior insights into product and business strategy. Build AI-enabled self-service reporting, improve tracking and data quality, and develop scalable ETL pipelines while partnering with Product, Engineering, Finance, Marketing, and Business Intelligence teams.

Leads quantitative analytics and data science initiatives for co-brand credit card products. Translates business problems into analytical solutions, builds statistical, machine learning, and generative AI models, analyzes large datasets, and communicates insights to stakeholders. Develops analytics strategies and standards, partners across the Card organization, and delivers scalable solutions that improve business and customer outcomes.

Own marketing measurement strategy for Paid Social, Programmatic, and Paid App channels. Design experimentation, incrementality, attribution, and marketing mix modeling frameworks; analyze data with SQL, Python, and Tableau; identify growth opportunities; and translate findings into recommendations for senior leadership and multimillion-dollar investment decisions. Partner cross-functionally to implement scalable measurement solutions, establish analytics standards, influence marketing ROI practices, and mentor team members.

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.