below marketmost similar roles pay hereabove market
This role pays more than
95%
of similar roles. Most pay
$144,650–$214,500
— the blue band above.
At the midpoint, this role pays about
$254k
versus about
$180k
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.
JOB TITLE: (USA) Distinguished, Data Scientist
The Distinguished, Data Scientist joins the team advancing advertising measurement to lead the design and development of a next-generation ads incrementality measurement platform. This role involves owning methodologies end-to-end, designing large-scale causal inference models to measure true incremental lift across various channels, and developing event-grain incrementality models like Uplift DNN to power ads bidding optimization. You will establish evaluation frameworks, author technical whitepapers, and mentor scientists while partnering with engineering to build data pipelines. The position requires deep expertise in randomized control trials, PIE models, instrumental variables, and regression discontinuity. Candidates must demonstrate proficiency in Python, SQL, and distributed data platforms such as Spark or BigQuery. The work focuses on solving complex measurement problems by grounding ads performance in rigorous, causally grounded science.
What does a Data Scientist earn in California?
Median $214500 from 134 postings across 32 companies.
Design and implement large-scale causal inference methodologies to measure incremental lift across advertising channels.
Develop event-grain incrementality models, such as Uplift DNNs, to power ads bidding optimization.
Establish evaluation frameworks, exit criteria, and solution readiness standards for causal inference methodologies.
Define the science roadmap for incrementality and prioritize high-leverage measurement problems.
Partner with engineers to design data pipelines and schemas that meet high-precision requirements at scale.
Author high-quality technical design documents and whitepapers for presentation to senior leadership.
Mentor mid-level and senior scientists while developing the team's technical vision and strategy.
What we're looking for
Obtain a Bachelor's degree with 6 years of analytics experience, a Master's degree with 4 years of analytics experience, or 8 years of analytics experience.
Possess deep, specialized 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.
Maintain proficiency in Python and SQL for large-scale data analysis and experience with distributed data platforms like Spark or BigQuery.
Possess 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).
Experience with open source frameworks such as scikit-learn, TensorFlow, or Torch (preferred).
Python
SQL
Machine Learning
AWS SageMaker
MLOps
Spark
TensorFlow
scikit learn
R
Scala
Deep Learning
Data Visualization
Statistical Modeling
Big Data Analytics
Generative AI
Data Governance
WCAG) 2.2 AA