Data Scientist II

Uber

Hybrid

Quick summary

Work type: Hybrid
Location: Sunnyvale, CA
Salary: $174,304–$209,164 / yr
Posted: 3 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $152k

This role $192k

$86k most similar roles pay here $222k

This role pays more than 80% of similar roles. Most pay $126,800–$177,968 — the shaded band above. At the midpoint, this role pays about $192k versus about $152k for comparable roles.

Based on 240 similar postings.

Employer

About Uber

Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.

Uber currently has 45 open roles on FindRole.

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At a glance

TL;DR · Data Scientist II

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As a Data Scientist II at Uber Technologies in Sunnyvale, California, you will join the Data Science team to perform statistical analyses and develop fraud detection features and models. Your day-to-day responsibilities include building and maintaining fraud rules, extracting insights from large datasets, conducting experiments to optimize risk mitigation strategies, and presenting findings to management for informed decision-making. You will need proficiency in R or Python, SQL, experimentation techniques like A/B testing, and quantitative modeling including machine learning. The role requires a deep understanding of risk data and metrics, with experience in fraud detection and payments systems. This fast-paced environment demands engagement and proactive problem-solving skills as you collaborate globally to mitigate fraudulent activities.

Skills

Python R SQL A/B testing descriptive statistics regression machine learning time-series forecasting KPIs risk analysis fraud detection payments processing

What you'll do

Perform statistical analyses to understand and mitigate risk and fraud behaviors.
Build and maintain fraud detection rules based on evolving fraudulent activities.
Extract insights from large data sets to develop new strategies against fraud.
Develop expertise in risk data, reporting, and key metrics for business decisions.
Conduct experiments to optimize the effectiveness of risk mitigation solutions.
Present findings to management to support strategic decision-making processes.

What we're looking for

Master's degree in Mathematics, Statistics, Computer Science, Operational Research, Economics, or related field.
2 years of experience in data science, risk management, fraud detection, or similar roles.
Proficiency in R or Python for data analysis and modeling.
Expertise in SQL for database queries and data extraction.
Experience with statistical analysis techniques including regression, correlation, and confidence intervals.
Knowledge of quantitative modeling methods such as machine learning and time-series forecasting.
Ability to develop metrics and KPIs for performance measurement and risk mitigation.

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