Senior Lead Data Science Engineer, Risk

We’re defining what it means to build and deliver the most extraordinary sports and entertainment experiences. Our global team is trailblazing new markets, developing cutting-edge products, and shaping the future of responsible gaming.

Here, “impossible” isn’t part of our vocabulary. You’ll face some of the toughest but most rewarding challenges of your career. They’re worth it. Channeling your inner grit will accelerate your growth, help us win as a team, and create unforgettable moments for our customers.

The Crown Is Yours

Our team comprises algorithm experts and data science technologists, coming together to develop innovative data products that solve analytically challenging problems at DraftKings. Risk data science at DraftKings directly influences revenue, customer trust, and regulatory compliance. As a Senior Lead Data Science Engineer, Risk on this team, you’re not just optimizing metrics—you’re enabling safe play at scale. Your contributions will harden our defenses, reduce financial exposure, and support responsible platform growth.

What you'll do as a Senior Lead Data Science Engineer, Risk

• Apply deep statistical reasoning and analytical creativity to detect, model, and counter evolving fraud patterns—where ground truth is ambiguous and adversaries adapt in real time

• Orchestrate end-to-end ML lifecycle planning across teams, with a focus on deeply analytical modeling approaches—ensuring solutions are mathematically grounded, interpretable, and resilient to adversarial manipulation.

• Design and implement scalable ML pipelines for real-time and batch environments, including robust testing, statistical validation, and data/application monitoring strategies to detect and mitigate adversarial behaviors such as payment abuse and stolen identities.

• Lead cross-functional technical strategy in how DraftKings builds, deploys, and maintains machine learning systems for fraud detection, trust, and identity verification, ensuring performance, scalability, and reliability across domains.

• Set the architectural and technical vision across teams, translating risk detection needs into measurable, production-ready outcomes—while shaping the future technical direction of our ML risk platform in close partnership with Risk Ops, Product, Legal, and Engineering.

• Drive process innovation in model development and evaluation, including backtesting frameworks, experimentation infrastructure, and system-level reporting to accelerate iteration and reduce model risk.

• Break down large, ambiguous initiatives into multi-team, technically executable plans, ensuring timely delivery across multiple sprints.

• Mentor senior engineers and technical leads across domains, setting modeling standards and fostering organizational excellence through scalable design reviews and capability-building.

What you'll bring

• 8+ years of engineering experience, including 5+ years building and operating ML/AI systems at scale in production environments—especially in adversarial domains like fraud, risk, or abuse detection.

• Deep mastery of applied machine learning and statistical modeling, including the ability to formulate ambiguous problems with mathematical rigor and deliver accurate, interpretable solutions under real-world constraints.

• Exceptional critical thinking skills and a proven ability to design robust models in adversarial settings, where data is noisy, incomplete, or intentionally deceptive.

• Expert-level Python skills, with fluency in ML tooling and experience in other languages or frameworks that support scalable deployment.

• Hands-on experience with modern ML infrastructure and platforms, such as Databricks, Kubernetes, Terraform, Kafka, and MLflow.

• Track record of mentoring leads and scaling technical rigor across teams, coupled with high autonomy and the ability to drive cross-cutting strategic initiatives.

• Executive-level communication and influence, capable of aligning technical direction with cross-functional leadership across Legal, Risk Ops, Product, Compliance, and Engineering to deliver platform-wide outcomes.

• A commitment to continuous learning, with a passion for staying up to date on emerging ML technologies and fraud tactics in evolving risk landscapes.

• A Master's or PhD in a relevant field such as Computer Science, Statistics, or Mathematics is preferred.

Join Our Team

We’re a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As a regulated gaming company, you may be required to obtain a gaming license issued by the appropriate state agency as a condition of employment. Don’t worry, we’ll guide you through the process if this is relevant to your role.