Senior Data Scientist, Client Insights

Alloy is where you belong!

Alloy helps solve the identity risk problem for companies that offer financial products by enabling them to outpace fraud and confidently serve more people around the world. Over 700 of the world’s largest financial institutions and fintechs turn to Alloy to take control of fraud, credit, and compliance risk, and grow with the clearest picture of their customers.

Through our values: Be Bold, Get Scrappy, Collaborate, and Celebrate Our Differences, we are creating a workplace where you can grow, thrive, and belong. See how we’ve been continuously recognized and named one of Inc. Magazine’s Best Workplaces, Forbes America’s Best Startup Employers, Best Fintech to Work for by American Banker, year after year.

Check out our investors and read more about us here.

About the team

The Client Insights team focuses on helping clients get the most value out of their data. We rely on analytics and machine learning to help clients improve their policies, better detect fraud, and stay up to date with industry best practices. The Client Insights team is made up of data scientists and full stack engineers who are working to deliver customer-facing tools that leverage client data to enhance the agent experience and quicker detect fraud. 

Alloy operates in a hybrid-work environment. We look to foster collaboration and community by having our local employees onsite twice a week, and remote employees onsite once a quarter.

What you’ll be doing

At Alloy, we aim to help our clients protect themselves from the rapidly increasing and ever-changing threat of fraud, and our Client Insights team is part of the core foundation supporting that effort. With thousands of attributes coming from our portfolio of data partners tied to hundreds of millions of entities across our customer base, we have a massive trove of data assets at our disposal that we’ve only just begun to take advantage of. As a Senior Data Scientist on Client Insights, you will own and evolve critical components of Alloy’s predictive modeling ecosystem. This is a hands-on senior individual contributor role for someone who blends deep modeling expertise with strong production engineering instincts and product intuition.

Your responsibilities will be to:

• Design, develop, and deploy advanced statistical and machine learning models that directly impact fraud detection and risk decisioning across Alloy’s platform.
• Own the end-to-end model lifecycle — from problem framing and feature engineering to deployment, monitoring, retraining, and performance optimization.
• Build high-quality, production-ready code including feature pipelines, training workflows, evaluation frameworks, and observability tooling.
• Partner closely with Engineering and ML Platform teams to ensure models are scalable, reliable, and seamlessly integrated into real-time and batch decision systems.
• Improve Alloy’s experimentation capabilities by designing statistical tests, building evaluation frameworks, and translating results into clear, actionable recommendations.
• Advance our ML capabilities by introducing reusable modeling components, improving documentation standards, and contributing to long-term roadmap planning.
• Conduct exploratory research and bespoke analyses to unlock new fraud prevention strategies and support future product innovation.
• Provide technical leadership and mentorship to other data scientists and cross-functional partners.

Who we’re looking for

You are:

• Product-minded and business-oriented — you understand how modeling decisions translate into customer and company impact.
• Fluent in both technical depth and executive communication.
• Comfortable turning ambiguous business problems into measurable modeling strategies.
• Passionate about building scalable, repeatable, and maintainable ML systems.
• Proactive, curious, and driven to raise the bar on data science quality and rigor.

You have:

• 6+ years of experience in data science, applied statistics, or machine learning in a production environment.
• 2+ years leading complex projects or initiatives.
• Expert-level proficiency in Python (pandas, NumPy, scikit-learn, etc.) and strong SQL skills.
• Experience developing and deploying production models, including monitoring, retraining, and performance management.
• Strong fou