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Econometrics Causal Inference Jobs (NOW HIRING)

PandaDoc

Staff GTM Data Scientist

Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods ...

Amazon

Sr. Economist, Pricing Science

Seattle, WA · On-site

$104K - $132K/yr

Provide causal inference guidance on pricing experiment questions as they arise - being the methodology resource when experiments generate LTV-relevant questions * Serve as cross-team economic ...

Apple

Applied ML Science Manager

Cupertino, CA

$198K - $342K/yr

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical ...

Apple

Applied Scientist

Austin, TX · On-site

You will be at the forefront of designing, developing, and deploying cutting-edge Causal Inference ... Master's degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science ...

Walmart

Senior Manager, Advanced Analytics

Oakland, CA · On-site

$117K - $234K/yr

Experience with experimentation, causal inference, forecasting, econometric modeling, or marketing/investment measurement. * Experience translating analytical findings into executive-level ...

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Econometrics Causal Inference information

What is econometrics causal inference?

Econometrics causal inference is a field within econometrics that focuses on identifying and quantifying cause-and-effect relationships using statistical methods and economic theory. Unlike simple correlations, causal inference aims to determine whether a particular action or policy actually causes a specific outcome. This often involves using methods like randomized controlled trials, instrumental variables, difference-in-differences, or regression discontinuity designs to address issues like confounding and bias. Econometricians working in causal inference design studies and analyze data to provide robust evidence for policy-making, business decisions, and academic research.

What is the difference between Econometrics Causal Inference vs Data Analyst?

AspectEconometrics Causal InferenceData Analyst
Required CredentialsMaster's or PhD in Economics, Statistics, or related fieldsBachelor's degree in Data Science, Statistics, or related fields
Work EnvironmentResearch-focused, academic or policy settingsBusiness, marketing, or operational environments
Employer & Industry UsageUniversities, government agencies, research institutionsCorporations, consulting firms, marketing agencies
Common Search & Comparison IntentUnderstanding causal relationships in dataAnalyzing data for insights and reporting

Econometrics Causal Inference specialists focus on identifying causal effects using advanced statistical methods, often in research or policy contexts. Data Analysts interpret data to generate reports and insights for business decisions. While both roles require strong analytical skills, Econometrics Causal Inference emphasizes causal modeling and rigorous statistical techniques, whereas Data Analysts focus on data interpretation and visualization.

What are the key skills and qualifications needed to thrive as an Econometrics Causal Inference Specialist, and why are they important?

To thrive as an Econometrics Causal Inference Specialist, you need strong quantitative analysis skills, a solid background in statistics or economics, and typically a graduate degree in a related field. Familiarity with programming languages like R, Python, or Stata, and experience with econometric modeling software are essential, along with knowledge of causal inference methods such as difference-in-differences or instrumental variables. Strong problem-solving abilities, critical thinking, and clear communication are standout soft skills for translating complex analyses into actionable insights. These competencies are crucial for accurately identifying causal relationships in data and making evidence-based recommendations that impact policy or business decisions.

What are some common challenges faced by professionals working in econometrics causal inference roles?

Professionals in econometrics causal inference roles often encounter challenges related to data quality, model specification, and identifying valid instruments for causal analysis. Ensuring that the assumptions underlying causal inference methods, such as no omitted variable bias or proper randomization, are met can be particularly difficult with real-world data. Collaboration with domain experts and data engineers is frequently necessary to properly interpret results and validate findings. Additionally, effectively communicating complex statistical concepts to non-technical stakeholders is a key part of the job.
Infographic showing various Econometrics Causal Inference job openings in the United States as of May 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 80% Physical, 3% Hybrid, and 17% Remote job distribution.
Senior Data Scientist - Growth Measurement

Senior Data Scientist - Growth Measurement

Roblox

San Mateo, CA • On-site

Other

Posted 20 days ago

Job description

WHY DATA SCIENCE & ANALYTICS?

The Data Science & Analytics organization's mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling and machine learning. Aligned and partnering with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure impact on our community of players and developers.

WHY GROWTH MEASUREMENT?

In this role, you will leverage your expertise in data science, statistics, and causal inference to develop measurement framework and product to deepen our quantitative understanding of growth efforts, which includes but not limited to performance marketing efficacy, attribution modeling and incrementality measurement. You will collaborate with cross-functional teams to develop and implement data-driven products and strategies that maximize the impact of our growth initiatives. You will also report to Senior Data Science leadership on the team.

You Will:
  • Conduct comprehensive analyses and develop measurement framework for growth efforts using advanced statistical methods and causal inference methodologies
  • Collaborate with marketing, product, and engineering teams to find opportunities for improvement and build data-driven solutions.
  • Contribute to the development of a robust data infrastructure to support user growth.
  • Communicate insights and discuss recommendations with cross function partners translating complex technical concepts into actionable insights.
  • Partner with different product teams to optimize user growth strategies through insights, strategy, and leadership.
You Have:
  • A MSc, PhD, or equivalent experience in Statistics, Economics, Operations Research, Computer Science, Applied Math, Physics, Engineering, or other quantitative fields.
  • 4+ years of experience in a data science role, with a focus on marketing science and campaign evaluation.
  • Strong knowledge and practical application of statistical methods, causal inference techniques, and experimental design.
  • Experience working with large datasets and proficiency in SQL, R/Python, and data visualization tools.
  • Experience in the gaming industry and/or multisided marketplaces.

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