Applied Machine Learning Engineer - Causal Inference Recommendation

Welcome to DoorDash, where we are passionate about connecting people with their favorite restaurants and revolutionizing the way food is delivered. As an Applied Machine Learning Engineer for Causal Inference Recommendation, you will play a crucial role in helping us improve our recommendation systems and drive business growth. We are looking for a highly skilled and experienced individual who is... well-versed in machine learning techniques and has a strong understanding of causal inference. If you are a problem-solver, team player, and have a passion for using data to drive business decisions, we would love to have you join our team. Develop and implement machine learning models and algorithms to improve DoorDash's recommendation systems. Conduct research and analysis to identify opportunities for improving the efficiency and accuracy of our recommendation systems. Collaborate with cross-functional teams including data scientists, software engineers, and product managers to design and implement machine learning solutions. Stay updated on the latest advancements in machine learning and causal inference techniques and apply them to enhance our recommendation systems. Analyze and interpret large datasets to identify patterns and insights that can be used to optimize our recommendation systems. Work closely with the business team to understand their needs and provide data-driven recommendations for business growth. Monitor and evaluate the performance of our recommendation systems and make necessary improvements to ensure their effectiveness. Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner. Contribute to the development and maintenance of best practices and standards for machine learning at DoorDash. Mentor and provide guidance to junior team members to help them improve their machine learning skills. DoorDash is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics