Applied Machine Learning Scientist – Uber Eats (NYC) 1800 views

Are you interested in working at the intersection of machine learning (ML), software engineering, and data science? Do you have an interest in developing and applying quantitative solutions to uniquely challenging problems on Uber Eats? If so, then this is the job for you.

About You

• Strong expertise in machine learning. Prior research or industry experience in Natural Language Processing (NLP), Deep Learning, Reinforcement Learning, Active Learning, bandit-based optimization preferred.
• Superb quantitative background (e.g. machine learning, statistics, mathematics, or computer science). Graduate degree required.
• Bias towards action and impact – able to structure a project from idea, prototyping, productionization to impact quantification. Industry experience with a proven track record of delivering impactful results preferred.
• Familiarity with technical tools including Tensorflow/PyTorch and Hive/Spark. Previous software engineering background a plus.
• Passionate and attentive self-starter, great communicator, amazing follow-through – you have a great work ethic and love the responsibility of being held accountable for the results.

About The Team

Uber Eats Data Scientists help solve the most challenging problems related to Uber’s ambitious and rapidly expanding on-demand food delivery businesses, which currently operates in more than 45 countries globally and is the largest outside of China. These fascinating and difficult problems include personalized search and recommendation for restaurants and dishes, travel and food preparation time prediction, text mining and natural language processing, demand and supply forecasting, growth and spend optimization, dynamic pricing, dispatch and routing optimization, and many more.

Below is a list of subdomains within the Restaurant team:

• Menu optimization: They leverage state-of-the-art algorithms to efficiently train supervised and semi-supervised models, with the goal of optimizing Menus’ content, structure, prices, and visibility.
• Knowledge Graph: To enhance the recommendation and search capabilities, they build an extensive knowledge graph to capture the relationship between food, restaurants, users and other marketplace entities using a wealth of data unique to Uber and Uber Eats.
• Recommendation: They help restaurants optimize their online presence to boost their business and achieve their goals, finding win-win operational goals for both Uber Eats and their restaurant partners.