Top Hybrid Data Science Jobs

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.

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.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.

The Senior Data Scientist will analyze product, customer, and risk data to improve Support and Risk Operations. Responsibilities include defining metrics, forecasting demand, modeling staffing and queue health, evaluating experiments and policies, monitoring risk models for bias and drift, and communicating decisions to product, engineering, and risk stakeholders. The role also leads technical standards, develops reusable analytical tooling, mentors data scientists, and uses AI tools and agent workflows to improve analytical quality and efficiency.

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.

Paid 10-week AI and data science internship supporting data analysis, dashboards, machine learning, generative AI, LLM, and AI application projects. Interns will work with technical and business stakeholders, research emerging technologies, develop prototypes, and present insights within the asset management industry. Undergraduate and master’s tracks offer exposure to business intelligence, cloud platforms, financial technology, and production-ready AI solutions.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

The Data Scientist Intern will support Network program execution through data modeling, transformation, analysis, visualization, reporting, and process tooling. Responsibilities include building operational insights and business narratives, improving program observability and accountability, supporting compliance and auditability, collaborating with cross-functional stakeholders, and contributing to special projects. The intern will also participate in mentorship, training, and professional development activities.

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.

Develop valuation and forecasting models, including customer lifetime value, net present value, and offer economics, to guide marketing investments. Analyze customer, offer, channel, and marketing data; perform data preparation, exploratory analysis, modeling, validation, experimentation, and performance monitoring. Partner with marketing, finance, and data teams to communicate insights and support data-driven recommendations.