**About the Role** The Uber Maps team is at the forefront of advancing Uber’s geospatial technologies, crucial for driving the efficiency and reliability of Uber services. We work across a diverse array of problem domains, including: curating and improving precision of location (i.e. ‘Places’) data, developing cutting-edge location search algorithms for each Rides pickup/dropoff and Eats delivery, building base maps and correcting map errors, as well as optimizing routes and travel time predictions, and more. These pivotal technologies are the backbone of every decision made in our marketplace, influencing dispatch and pricing strategies directly. If you're passionate about driving innovation and have relevant experiences in tech or marketplace settings, we sincerely welcome you to join our team and shape the future of Uber's geospatial technologies together. You can learn more about our team from: - [Fixing Map Errors with GPS Data](https://eng.uber.com/mapping-accuracy-with-catchme/) - [What’s my ETA? A Billion $ question](https://youtu.be/FEebOd-Pdwg) - [Rethinking GPS: Engineering Next-Gen Location at Uber](https://eng.uber.com/rethinking-gps/) - [Enhancing the Quality of Uber’s Maps with Metrics Computation](https://eng.uber.com/maps-metrics-computation/) **Basic Qualitifactions** - Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathemathics, Computer Science, Machine Learning, Operations Research, or other quantitative fields. - 4+ years of industry experience as an Applied or Data Scientist or equivalent. - Background in at least one programming language (eg. R, Python, Java, Ruby, Scala/Spark or Perl) - Coding and SQL proficiency and ability to develop statistical analysis and algorithm prototyping in Python or R. - Ability to use Python, SQL, R or similar technologies to work efficiently with large data sets - Design experiments and interpret the results to draw detailed and actionable conclusions across a variety of key performance indicators **Preferred Qualifications** - Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams - Experience leading key technical projects and substantially influencing the scope and output of others - Knowledge of experimental design and analysis or experience with exploratory data analysis and model development - Experience communicating qualitative research methods and findings to non-qualitative researchers - Track record of engaging senior leadership effectively to build understanding of and consensus for the viewpoints of the team - Solid theoretical and applied ML skills and a strong background in mathematics and stats - Solid Programming skills to prototype models in at least one of Python (preferably), R, Java, Go, Scala - Expert in one of the following areas: Deep learning, ML System Design, A/B experimentation design, Causal Inference - Experience of working with large dataset using Spark, Hive, HDFS is desired - Analyze large data sets to identify behavior trends among good users and bad actors, using statistics, data mining, and machine learning techniques - Thought leadership to drive multi-functional projects from concept to production We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let’s move the world forward, together. Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role. \*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [accommodations@uber.com](mailto:accommodations@uber.com).