Principal Data Scientist

Walmart

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Sunnyvale, CAHoboken, NJ
Salary: $143,000–$286,000 / yr
Employment: Full-time
Posted: 24 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $190k

This role $214k

$115k $304k

below market most similar roles pay here above market

This role pays more than 87% of similar roles. Most pay $165,000–$214,500 — the blue band above. At the midpoint, this role pays about $214k versus about $190k for comparable roles.

Based on 240 similar postings.

Employer

About Walmart

Walmart Inc. is the world''s largest retailer by revenue, operating a chain of hypermarkets, discount department stores, and grocery stores, as well as a growing e-commerce presence through Walmart.com. Industry: General Merchandise & Grocery Retail

Walmart currently has 719 open roles on FindRole.

Listed pay typically runs $117,000–$234,000 across 592 roles with salary data.

Most-posted roles

View all roles at Walmart

At a glance

TL;DR · Principal Data Scientist

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JOB TITLE: (USA) Principal, Data Scientist The Principal Data Scientist joins the team developing advanced advertising measurement solutions to lead the design and development of a next-generation ads incrementality measurement platform. This role involves owning methodologies end-to-end, including designing and implementing large-scale causal inference models to measure true incremental lift across various channels and segments. You will develop event-grain incrementality models like Uplift DNN to power bidding optimization, establish evaluation frameworks, and mentor other scientists. The position requires deep expertise in randomized control trials, PIE models, instrumental variables, and regression discontinuity. You will utilize Python, SQL, and distributed data platforms such as Spark or BigQuery. The work focuses on solving complex measurement problems by grounding rigorous, causally grounded ads measurement in data validations and technical design documents.

What does a Data Scientist earn in California?

Median $214500 from 134 postings across 32 companies.

See salary data

Skills

Causal Inference Python SQL Spark BigQuery Machine Learning TensorFlow scikit learn R Scala RCT Uplift DNN Experiment Design Distributed Data Platforms Econometrics

What you'll do

Design and implement large-scale causal inference methodologies to measure incremental lift across various advertising channels.
Develop event-grain incrementality models, such as Uplift DNNs, to power ads bidding optimization decisions.
Establish evaluation frameworks, exit criteria, and solution readiness standards for causal inference methodologies.
Define the science roadmap for incrementality and prioritize high-leverage problems and principled trade-offs.
Partner with engineers to design data pipelines and schemas that meet precision requirements for causal measurement.
Author high-quality technical design documents and whitepapers to present to senior leadership.
Mentor senior and mid-level scientists on complex measurement problems and technical execution.

What we're looking for

Obtain a Bachelor's degree with 5 years of analytics experience, a Master's degree with 3 years of experience, or 7 years of experience.
Possess deep expertise in causal inference and incrementality-based optimization, including RCTs, PIE models, and instrumental variables.
Demonstrate strong command of experiment design at scale, including power analysis, variance reduction, and bias correction.
Proficiency in Python and SQL for large-scale data analysis and experience with distributed data platforms like Spark or BigQuery.
Strong statistical foundations and machine learning knowledge.
PhD in Statistics, Economics, Econometrics, Computer Science, or a related quantitative field (preferred).
Experience in the ads industry (preferred).
Successful completion of assessments in Python, Spark, Scala, or R (preferred).

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