Sr Applied Scientist, Delivery Experience

    • Uber Corporate
  • San Francisco, CA
  • Posted 60+ days ago | Updated 3 hours ago

Overview

On Site
USD 174,000.00 - 193,500.00 per year
Full Time

Skills

Machine Learning Operations (ML Ops)
Machine Learning (ML)
Inventory management
Programming languages
Design of experiments
Data Analysis
Statistics
Computer science
Operations research
Retail
Inventory
Data
Collaboration
Algorithms
Economics
Research
SQL
R
Apache Spark
Python
Testing
Operations
Transportation
Science
Law
Legal

Job Details

About the Team
The Delivery Experience team works to improve Uber's delivery experience under the Grocery and Retail segments. The team is working on various impactful initiatives, such as improving shopper efficiency, building the capability of accurately predicting inventory and the overall selection and replacement of items, and optimizing the delivery core experience from both the shopper and consumer side.
About the Role
We are looking for applied scientists with a passion for solving new and difficult problems with data. As a Senior Applied Scientist your responsibilities will include, but not be limited to, the following:
  • Build and own Machine Learning models used for inventory management
  • Conduct deep dive analysis to understand new opportunities, data source, and methodologies to improve our Machine learning models for inventory management
  • You will collaborate with other scientists, product managers, and business teams to understand the challenges in our space, then tackle problems that no one else has solved yet.
  • Deliver end-to-end solutions rather than algorithms
  • Partner closely with the engineers on the team to productionize, scale, and deploy your models world-wide.
Basic Qualifications
  • Bachelor's degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
  • 2+ years of industry experience as an Applied Scientist or equivalent.
  • Experience with machine learning, statistical methods, and causal inference.
  • Professional experience with programming languages and tools like SQL, R, and Spark.
  • Experience using Python to work efficiently at scale with large data sets
  • Experience with experimental design and analysis.
  • Experience with exploratory data analysis, statistical analysis and testing, and model development.
Preferred Qualifications
  • Ph.D. or M.S. degree in Computer Science, Machine Learning, Statistics, Operations Research, Economics,or other quantitative fields.
  • Experience to build and maintain production models using Python.
  • Experience in inventory management and prediction.
  • Experience in algorithm development and deploy the production model.
  • Ability to work closely with cross-functional stakeholders to implement decisions.
  • Deep understanding of operations and/or transportation science
  • Strong product intuition
For San Francisco, CA-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link .

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing .

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.