DraftKings
Senior Lead Trading Strategist
Remote - US · Senior · Full-time
Sponsorship not specifiedDetected 36 days ago
PythonJavaC++C#ReactMachine LearningPyTorchPandasNumPyResearchLeadershipCommunication
About the role
- It's transforming how we enhance customer experiences, streamline operations, and unlock new possibilities.
- Our teams are energized by innovation and readily embrace emerging technology.
- We're not waiting for the future to arrive.
Responsibilities
- Lead the design, implementation, and continuous refinement of market-making and trading strategies informed by market microstructure, participant behavior, and risk constraints.
- Develop and oversee real-time decision engines that price markets, optimize inventory, and react to high-frequency event streams.
- Strong leadership and communication skills, with a demonstrated ability to lead complex technical initiatives and influence cross-functional teams.
Requirements
- Strong proficiency in Python (including NumPy, Pandas, and PyTorch) and experience with C++, C#, Java, or Rust for performance-critical systems.
- 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.
Compensation
- The US base salary range for this full-time position is 184,000.00 USD - 230,000.00 USD, plus bonus, equity, and benefits as applicable.
Benefits
- Communicate strategy vision, system behavior, and business impact clearly to technical and non-technical stakeholders, influencing decision-making across teams.
- At least 7 years of experience in machine learning, quantitative modeling, or algorithmic strategy development.
- The US base salary range for this full-time position is 184,000.00 USD - 230,000.00 USD, plus bonus, equity, and benefits as applicable.
- Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
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This listing is sourced directly from DraftKings's careers page and normalized into a canonical job model.