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Lead Analyst – Risk

Lead Analyst – Risk

CompanyDraftKings
LocationBoston, MA, USA, New York, NY, USA
Salary$115900 – $144900
TypeFull-Time
DegreesBachelor’s
Experience LevelSenior

Requirements

  • At least 5 years of experience in analytics or data science, with a minimum of 2 years specifically in risk analytics, fraud, or financial crime prevention.
  • Bachelor’s degree or equivalent in Mathematics, Statistics, Economics, Computer Science, Engineering, Business Analytics, or a related field.
  • Deep understanding of risk management principles, including experience applying analytics to assess, detect, and mitigate risk in high-volume, high-stakes environments.
  • Proven ability to take complex problems and data sets, build structured frameworks, and present clear, actionable insights to cross-functional teams and senior leadership.
  • Advanced proficiency in SQL/Snowflake, Tableau (or similar data visualization tools), and Microsoft Excel and/or Google Sheets.
  • Solid understanding of statistics, hypothesis testing, and experimental design.

Responsibilities

  • Own and lead high-impact analytical workstreams focused on evaluating fraud prevention workflows, analyzing chargeback trends, and enhancing risk mitigation strategies.
  • Monitor and respond to emerging threats by analyzing real-time alerts, identifying behavioral patterns, and developing data-driven solutions to reduce risk exposure.
  • Translate complex analyses into clear, actionable insights and recommendations that align stakeholders and drive effective risk management.
  • Build and maintain reporting tools and dashboards that surface key risk metrics, inform strategic decision-making, and support continuous process improvements.
  • Collaborate closely with cross-functional teams including Product, Engineering, Risk Operations and Data Science to analyze customer behavior, surface risks, and guide strategic initiatives.

Preferred Qualifications

  • Experience with Python, R, or statistical programming languages is a plus.
  • Experience with predictive modeling is a plus.