Top Hybrid Data Science Jobs

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Data Science Interns collaborate with product and engineering teams to analyze data, build models, support A/B tests and causal inference studies, maintain analytical datasets and pipelines, and communicate actionable insights. The role involves solving complex business problems with senior data scientists and applying responsible generative AI to improve workflow efficiency, cost, and quality. This is a 12-week summer 2027 internship with quarterly in-person work sessions.

Own Fora’s growth operating model for forecasting, scenario planning, goal-setting, and strategic investment decisions. Partner with Strategy & Operations, Finance, founders, and executives to analyze business trends, size opportunities, model pricing and initiative impacts, and guide resource allocation. Mentor data team peers while establishing best practices, tools, processes, and standards.

Build and deploy predictive, forecasting, pricing, promotion, and machine learning models using large-scale retail data. Apply statistical modeling, causal inference, and experimentation to support demand forecasting, inventory planning, merchandising, and supply chain decisions. Partner with Product, Engineering, Analytics, and Business teams to define requirements, evaluate model performance, and translate complex findings into actionable recommendations and production-ready solutions.

Summer intern assisting with IT solution design and implementation, supporting cybersecurity initiatives through security protocol analysis, and collaborating with data science teams to analyze data and generate actionable insights. The role offers hands-on experience, mentorship, and professional development within Honeywell Aerospace. Candidates must remain enrolled in a relevant degree program, maintain at least a 3.0 GPA, and meet U.S. person requirements.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Apply statistical inference and experimentation to evaluate product changes, identify Growth opportunities, and guide strategy. Partner with product, engineering, and marketing leaders to define metrics, build datasets and dashboards, and communicate insights that drive business and product decisions. The role requires SQL, scripting, data transformation, and practical tradeoffs between speed and accuracy. Predictive modeling experience is preferred, as are startup and B2B SaaS backgrounds.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.