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Causal Inference Jobs in Massachusetts (NOW HIRING)

Roku

Senior Research Data Scientist

Boston, MA · On-site

$330K - $375K/yr

Design, build, and productionize a causal inference platform that standardizes how Roku measures the incremental impact of customer actions and business decisions * Research and implement causal ...

UMass Med School

Post Doc - Open Rank

Worcester, MA · On-site

$75K/yr

Additional Information Postdoc in Causal Inference of Complex Gene Networks We invite applications for a NIH-funded postdoctoral researcher position in our computational lab at UMass Chan Medical ...

PhaseV

Senior Data Scientist

Boston, MA

Leveraging the power of advanced causal inference and pushing the boundaries of machine learning, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the ...

PhaseV

Senior Data Scientist

Cambridge, MA · On-site

Leveraging the power of advanced causal inference and pushing the boundaries of machine learning, PhaseV detects hidden signals in clinical data and extracts actionable insights for planning the ...

Fidelity Investments

Director, Data Science

Boston, MA · On-site

Apply causal inference techniques (e.g., uplift modeling, DiD, matched controls) to assess incrementality * Guide best practices for power analysis, inference, and result interpretation * Serve as a ...

Lennar

Lead Data Scientist

Boston, MA · On-site

They are fluent across the modern ML and applied AI stack-classical supervised learning, time-series forecasting, causal inference, recommendation systems, and LLM-based approaches-and they know ...

BioLink 360

Biostatistician

Boston, MA · On-site

In this role, you will be leading the design and execution of sophisticated statistical analyses grounded in causal inference and taking full ownership of statistical analysis plans from initial ...

UMass Med School

Computational Biologist

Worcester, MA · On-site

$90K/yr

Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks). * Combine data from ...

NorthEastern

Postdoctoral Research Associate

Boston, MA · Remote

$60K - $85K/yr

Applying epidemiologic, econometric, and other methods to strengthen causal inference * Working with multilevel, longitudinal data and quasi-experimental approaches * Exploring gender, racial/ethnic ...

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How much do causal inference jobs pay per year?

As of Jul 13, 2026, the average yearly pay for causal inference in Massachusetts is $108,372.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,900.00 and $118,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are the most commonly searched types of Causal Inference jobs in Massachusetts? The most popular types of Causal Inference jobs in Massachusetts are:
What are popular job titles related to Causal Inference jobs in Massachusetts? For Causal Inference jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Massachusetts look for? The top searched job categories for Causal Inference jobs in Massachusetts are:
What cities in Massachusetts are hiring for Causal Inference jobs? Cities in Massachusetts with the most Causal Inference job openings:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Massachusetts as of July 2026, with employment types broken down into 71% Full Time, and 29% Part Time. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $108,372 per year, or $52.1 per hour.
Postdoctoral Research Position in Causal Inference

Postdoctoral Research Position in Causal Inference

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Posted yesterday

Harvard University rating

8.4

Company rating: 8.4 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

80th of 553 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Research Position in Causal Inference
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
Position Description
We invite applications for a full-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims, linked with rich contextual information such as census data, weather records, and high-resolution air pollution and related environmental exposures data.
Motivated by relevant public health and policy questions, the goal is to develop methodologies for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span:
• Causal inference for spatiotemporal data,
• Methods for heterogeneous treatment effects estimation,
• Methods for multiple exposures, multiple outcomes,
• ML and AI methods for causal inference,
• Bayesian causal inference,
• methods for transportability and generalizability of causal effects across space, time, and populations.
Duties and Responsibilities
• Design, develop and implement novel causal inference methods in the areas listed in the position description.
• Work with large, high-dimensional datasets.
• Lead and contribute to manuscripts for high-impact journals (e.g., top Statistics journals and Nature-like journals).
• Present findings in internal meetings and at national/international conferences.
• Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists.
• Contribute to open-source code and reproducible pipelines.
Basic Qualifications
• PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field.
• Demonstrated expertise in causal inference, with interest in methods development.
• Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning, spatiotemporal modeling, high-dimensional statistics.
• Proficiency in statistical programming (R and/or Python) and good practices for reproducible research.
• Experience working with large datasets and cloud computing environments.
• Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career stage.
• Ability to work in a collaborative, interdisciplinary environment.
Additional Qualifications
Prior experience with one or more of:
• Health claims data, EHRs, or other large-scale health/administrative datasets.
• Environmental, climate, or air pollution exposure data.
Familiarity with LLMs.
Special Instructions
Please submit the following materials:
• Cover letter describing your research interests, relevant experience, and fit for this position.
• Curriculum vitae including a list of publications.
• One to three representative publications or preprints.
Names and contact information for 2-3 references.
Contact Information
Catherine Adcock
Contact Email
catherine_adcock@harvard.edu
Salary Range
$75,000
Minimum Number of References Required
2
Maximum Number of References Allowed
3
Keywords
Causal inference; spatiotemporal modeling; generalizability; transportability; environmental health

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