APAC Data Scientist II, Ops

APAC Data Scientist II, Ops Department: Data Science Team: Data Scientist Location: Bangalore, India Type: Full-Time **About the Role** We are looking for someone who gets excited solving tough business problems by applying scientific methods and bringing value from our extensive data resources. The APAC Data Science Ops team assists APAC Operations teams in addressing questions and resolving problems by employing data-driven methods and models. Our team is dedicated to supporting these teams by leveraging experimentation and quasi-experimentation to analyze the impact and outcomes of our actions. We also utilize modeling techniques to predict and optimize unresolved business challenges. Collaborating closely with the operations teams who implement these decisions, we engage in projects spanning from developing pricing models to assessing the impact of new product launches. Your primary responsibilities will revolve around these fundamental activities: aligning with business objectives, formulating clear problem statements, and managing data through collection, cleaning, and structuring. You will also conduct analytical explorations, apply techniques for inferential and predictive modeling, and effectively communicate the results. You will explore opportunities across a range of topics. This includes designing and executing experiments within the Uber marketplace and developing ML models for product launches. Additionally, you will work on Market Health models, Causal Inference models, and Optimization models for pricing or investment challenges, among other problems and techniques. **What the Candidate Will Do** Reporting to the Head of BizOps in Central Operations, this person will be responsible for: 1\. Experimentation, including designing and executing experiments to test / answer specific business questions, or to support launch and roll out plans of new products or features 2\. Causal Inference: using inference techniques to evaluate actions and policies, particularly in cases where experimentation is not an option. 3\. Modeling, including building or validating ex