Top Hybrid Data Science Jobs

The Manager of Data Science will lead a team focused on machine learning model development, team management, and cross-functional collaboration to advance product goals.

Lead and grow a data science team to build predictive models for credit risk and payments; mentor scientists; integrate DS work with product, engineering, and operations; run quarterly planning with stakeholders; drive adoption of AI-assisted and agentic workflows with validation and quality standards; and collaborate with modeling engineering to improve model research, development, and deployment infrastructure.

Lead Chewy Ads Data Science across onsite and offsite channels, owning strategy, roadmap, models, and experimentation. Build and mentor a team to deliver production ML systems for ranking, bidding, budget optimization, ROAS, and GenAI-powered advertiser tooling while partnering with Product and Engineering and representing science to senior leadership.

As a Data Scientist, you'll develop and validate machine learning models, implement AI solutions, and conduct fieldwork in support of customers.

Builds large, complex datasets and develops, trains, validates, and scales machine learning models and algorithms. Performs advanced statistical analysis, A/B testing, quasi-experimental analysis, and predictive modeling to support e-commerce initiatives such as targeting, optimization, and recommendations. Defines KPIs, identifies impactful customer cohorts, researches analytical techniques, creates dashboards, translates business needs into data requirements, and serves as a statistical modeling subject matter expert.

Develop and deploy scalable predictive models for Walmart’s fraud detection team using machine learning, deep learning, data mining, and anomaly detection. Analyze large real-time datasets, build dashboards, communicate insights, partner with product and business teams, own fraud and abuse risks, mentor colleagues, and contribute to research, automation, and potential patent filings.

Leads advanced data science initiatives, including model development, validation, tuning, deployment, monitoring, exploratory analysis, statistical modeling, and visualization. Translates business needs into scalable machine learning solutions, communicates insights to stakeholders, and mentors data science team members. The role supports Walmart+ membership growth through rigorous analytics, experimentation, and strategic decision-making.

Build and operate supply chain data pipelines, models, integrations, quality controls, and dashboards supporting procurement, inventory, logistics, suppliers, and project analytics. Partner with analysts and business teams to enable forecasting, planning, scorecards, KPI tracking, and data governance. Contribute to cloud data architecture, engineering standards, tooling implementations, and productionizing analytics models across AI infrastructure and capital-intensive supply chain operations.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Leads enterprise Key Drivers Analytics and an Associate Director team delivering advanced analytics across pharmaceutical business priorities. Owns analytics roadmaps, methodologies, governance, modeling standards, talent deployment, and globally distributed delivery. Directs multivariate analyses, segmentation, diagnostics, scenario modeling, validation, insight generation, and executive recommendations. Partners cross-functionally to translate evidence into measurable business actions while ensuring reproducibility, compliance, responsible AI, and scalable analytical solutions.

Lead product analytics and data science for a member platform, shaping product strategy through behavioral analysis, experimentation, segmentation, retention and churn modeling. Own product metrics end to end, build trusted datasets using Fivetran, dbt and BigQuery, and implement testing, monitoring, lineage and documentation. Partner cross-functionally with product, engineering, finance, operations and go-to-market teams to identify growth opportunities and support roadmap decisions.

Drive performance improvements for an AI advertising product through large-scale data analysis, metric development, system health evaluation, experimentation, and algorithm improvement. Analyze ad auction and user interaction data, diagnose bidding and machine learning systems, define success metrics, and collaborate with product, engineering, and applied science teams to guide product decisions and improve online prediction reliability, latency, and efficiency.