Data Scientist

About the Role

Our client is seeking a highly analytical Data Scientist to join a dynamic team focused on optimizing courier pay and incentive systems. This role sits at the intersection of machine learning, economics, and logistics, working on real-time models that impact operational efficiency and user experience.

Key Responsibilities

• Develop and validate machine learning models for courier compensation, including surge pricing, incentive programs, and earnings optimization.
• Build and engineer features from operational datasets to improve model performance.
• Design and execute experiments (A/B testing, switchback, geo-based) to measure the impact of pricing and incentive strategies.
• Translate complex analytical outputs into clear, actionable insights for stakeholders.
• Collaborate with ML Engineers and Operations Research teams on cross-functional initiatives.
• Apply best practices in coding, version control, testing, and model deployment.
• Automate model retraining, validation, and evaluation workflows.
• Monitor and maintain model performance in live production environments.
• Contribute to the development of real-time, low-latency decision systems.

Required Skills & Experience

• Hands-on experience building and deploying ML models in production.
• Strong proficiency in Python and SQL.
• Solid understanding of supervised learning techniques (regression, classification, gradient boosting).
• Experience with time-series modeling and predictive analytics.
• Strong foundation in experimental design and causal inference.
• Familiarity with ML Ops practices (Git, pipelines, monitoring).

Preferred Experience

• Exposure to real-time or low-latency systems.
• Experience in marketplace experimentation (e.g., switchback testing, interference-aware designs).
• Background or interest in incentive design, dynamic pricing, or labor economics.
• Experience solving problems in logistics or operations-focused environments.

What We’re Looking For

• Strong analytical and problem-solving mindset.
• Ability to balance business impact, fairness, and cost efficiency.
• Collaborative team player with strong communication skills.
• Passion for applying data science to real-world operational challenges.