Uber Applied Scientist Interview Guide

The short answer: Uber publishes one Sciences interview guide for Data Scientists, Applied Scientists and Scientists. According to Uber, the process is a 30-minute recruiter or sourcer call, 60 minutes of live coding with a team member, a 60-minute Jam Session, then 4 to 6 interviews of 45 minutes each with the team, all on Zoom. Your recruiter tells you which of eight competencies apply, such as Machine Learning, Experimentation/Statistics and Coding. Uber does not describe the Jam Session, and we found no readable candidate report that does. Current postings call the role Scientist II or Senior Scientist on the Applied Science subteam. Last reviewed October 2026.

Uber applied scientist interview: quick answers

The Uber applied scientist role

What Uber applied scientists work on, the skills its job postings ask for, and what a week can look like. Uber posts the role as Applied Scientist and Sr Applied Scientist in its interview guide and older postings; current postings use Scientist II and Senior Scientist with the subteam set to Applied Science.

Uber applied scientist levels and pay

Your level sets the bar you are interviewed against and the pay band of the offer.

Uber's Applied Science subteam postings use Scientist II and Senior Scientist. We found a Staff Scientist posting only on the Data Science subteam, so the Staff row shows that posting's bar and no pay.

Pay in Uber's US job postings

Uber's postings list a base salary range by location and mention bonus and equity on top. Both Applied Science subteam postings below were marked as removed when we read them.

A Manager II, Science posting on the Applied Science subteam lists $216K to $240K, left out because it is a people manager role. We found no Staff-level range on the Applied Science subteam. Ranges seen October 29, 2026.

Uber applied scientist interview process

Every step below comes from Uber's Sciences interview guide, which covers Data Scientist, Applied Scientist and Scientist roles. Candidate reports for this role were mostly unreadable, so little is known beyond Uber's stage names and competencies.

Based on Uber's own pages and candidate reports.

Uber does not publish a timeline. It says rescheduling is limited, so keep your scheduled times, and that all steps run on Zoom. If you receive a link to the online coding assessment or a shared coding environment, log in and have it open before the session. Last reviewed October 2026.

What is different at Uber

• One process covers every science title. Uber's Sciences guide is shared by Data Scientist, Applied Scientist and Scientist, and candidates are not assessed on all eight competencies: the recruiter says which apply to your role.
• The titles changed. Uber's guide and older postings say Applied Scientist and Sr Applied Scientist, but newer postings use Scientist II, Senior Scientist and Staff Scientist, with Applied Science or Data Science shown only as the subteam. Check the subteam field to know which track a posting is.
• The process includes a 60-minute Jam Session between live coding and the team interviews. Uber does not describe it, so ask your recruiter what to prepare.
• Applied science at Uber is heavily marketplace work. Postings ask for ML, optimization, causal inference and marketplace-dynamics models, plus experiment design, for teams such as delivery matching, Uber One membership, Maps, Experimentation and Uber Eats pricing.
• Uber says some scientists use data to inform business decisions while others build and deploy models that run systems in real time, and that most work end to end, owning metrics, experiments and technical solutions.
• The AI rule is strict and covers more than AI. Uber says not to use any external assistance, including search engines, documentation sites, collaboration with others, or AI tools.

Uber applied scientist interview questions

Questions from our bank, then practice prompts we wrote for this guide around Uber's work.

1from typing import List, Tuple
2
3def eta_correction_mae(train: List[Tuple[str, float, float]], test: List[Tuple[str, float, float]],
4                       min_trips: int) -> Tuple[float, float]:
5    """Return test MAE before and after a per-city residual correction."""
6    pass

1from collections import defaultdict
2from typing import List, Tuple
3
4def eta_correction_mae(train: List[Tuple[str, float, float]], test: List[Tuple[str, float, float]],
5                       min_trips: int) -> Tuple[float, float]:
6    if not train or not test:
7        raise ValueError("train and test must be non-empty")
8    sums, counts = defaultdict(float), defaultdict(int)
9    for city, eta, actual in train:
10        sums[city] += actual - eta
11        counts[city] += 1
12    global_mean = sum(sums.values()) / len(train)
13    residual = {c: sums[c] / counts[c] for c in counts if counts[c] >= min_trips}
14    before = after = 0.0
15    for city, eta, actual in test:
16        before += abs(actual - eta)
17        after += abs(actual - (eta + residual.get(city, global_mean)))
18    return round(before / len(test), 2), round(after / len(test), 2)
19
20# Complexity: O(n + m) time for n training and m test trips, O(c) space for c cities.

How to prepare

A plan for the Uber loop, most important first:

1. Ask your recruiter which of the eight competencies your loop covers and what the Jam Session involves, since Uber does not describe it.
2. Practice experiment design for marketplaces: power and sample size, switchback and cluster-randomized tests, and difference in differences.
3. Practice SQL and Python together for the 60-minute live coding round, including a data structures and algorithms problem.
4. Review ML for Uber's problems, such as ETAs, demand forecasting, pricing and fraud, from the model to the metric it moves.
5. Prepare one or two projects per competency, stating your own role and the technical trade-offs you made.
6. Write stories for Uber's values, such as Trip Obsessed and One Uber, and practice an elevator pitch of your career.

Practice path

Courses that cover the rounds above:

• Machine Learning
• Applied Statistics
• Data Structures and Algorithms
• ML System Design
• Advanced Machine Learning
• Deep Learning