Senior Data Scientist, Predict

About the team

The Predict team builds Alloy’s real-time machine learning systems at scale. Our immediate focus is on fraud detection, where we believe machine learning can simplify and accelerate decision-making in ways traditional rule-based systems can’t. Managing rules and policies to detect fraud is complex and constantly evolving; we use ML to make it smarter, faster, and more adaptive. Our approach is identity-centric, combining signals from a wide range of data sources to build a comprehensive understanding of risk.

You will work on advancing our core models while also partnering directly with customers to drive strong outcomes from fraud studies.

Alloy operates in a hybrid work environment. We look to foster collaboration and community by having our local employees onsite three days a week.

What you'll be doing

• Contribute to the design, training, and evaluation of machine learning models that power Alloy’s fraud detection capabilities.
• Develop testing plans, metrics, performance reports, and translate findings into actionable recommendations.
• Support production ML workflows, including feature generation, model training, and monitoring, to ensure models remain accurate and reliable at scale.
• Document findings and communicate insights to internal teams, contributing to shared learning and continuous improvement.
• Maintain up-to-date model documentation and support Alloy’s model governance processes to ensure transparency and compliance.
• Stay current with industry trends in applied ML and fraud detection, and contribute to Alloy’s mission of making financial services safer and more accessible.

Who we’re looking for

• Always building with end-solution in mind.
• Able to communicate complicated concepts to a non-technical audience without diluting the complexity of the work.
• Able to build strong cross-functional relationships within Alloy.
• Naturally curious with a knack for asking tough questions.
• Solid understanding of core ML concepts such as supervised learning, feature engineering, and model evaluation.
• A team player. You believe that big things happen when the right people are working together.
• A fast learner
• Humble. Mistakes happen and owning them helps us learn and move on quickly

You have:

• 8+ years as an individual contributor in Applied Fraud Research, Data Science, or Machine Learning with a proven track record in a “Solutions” or client-facing capacity.
• Expertise in working with highly imbalanced datasets and building production-grade Machine Learning models with specific interest in tree-based models.
• Advanced proficiency in scripting languages like Python and querying languages like SQL
• Proven ability to wrangle and think thoughtfully about data at scale (processing billions of records).
• Experience developing metrics and dashboards.
• Able to communicate their findings effectively to technical and nontechnical members
• Someone who embodies our shared Alloy values: be bold, get scrappy, collaborate, and celebrate our differences.
• You have experience in a highly analytical role in fast-paced environments
• Must be local to Greater New York City.

Nice to Haves:

• Previous experience in financial fraud detection.
• Advanced Degree (Masters or PhD) in a quantitative field.
• Experience managing the end-to-end lifecycle of a technical pilot or Proof of Concept.
• Experience with BI tools like Looker.
• Experience with modeling on graph structures.