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Causal Inference Jobs in Cambridge, MA (NOW HIRING)

Roku

Senior Research Data Scientist

Boston, MA

$330K - $375K/yr

About the role As a Senior Research Data Scientist on Roku's Data Science team, you will lead the development of a best-in-class causal inference platform that measures and optimizes the true ...

Roku

Senior Research Data Scientist

Boston, MA · On-site

$330K - $375K/yr

About the role As a Senior Research Data Scientist on Roku's Data Science team, you will lead the development of a best-in-class causal inference platform that measures and optimizes the true ...

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 ...

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 ...

phasevtrials.com

Senior Data Scientist

Boston, MA · On-site

$100 - $130/hr

The ideal candidate will have a strong foundation in machine learning and statistics with an interest in causal inference. A couple of recent preprints we've released to get a sense of the challenges:

New

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 ...

CookUnity

Director, Data Science/ML

Boston, MA · On-site

Develop causal inference methodologies to understand true incrementality of product changes. * Ensure models are observable, explainable where needed, and continuously improved post-launch Product ...

PhaseV

Biostatistics Researcher

Cambridge, MA

This role would have a dual mission of client-facing consulting and cutting-edge research in statistics, data science, causal inference, machine learning for modern clinical trial design and ...

Clinovo

Biostatistician Contractor

Waltham, MA · On-site

Provide statistical expertise in analyses involving external control arms, synthetic control methodologies, external comparator analyses, meta-analyses, and causal inference methods (e.g., propensity ...

PhaseV

Biostatistics Researcher

Cambridge, MA · On-site

This role would have a dual mission of client-facing consulting and cutting-edge research in statistics, data science, causal inference, machine learning for modern clinical trial design and ...

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All JobsCausal Inference JobsCausal Inference Jobs in Cambridge, MA

Causal Inference information

See Cambridge, MA salary details

$60.1K

$108.5K

$148.1K

How much do causal inference jobs pay per year?

As of Aug 8, 2026, the average yearly pay for causal inference in Cambridge, MA is $108,457.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,000.00 and $118,600.00 per year, depending on experience, location, and employer.

Is causal inference still relevant?

Causal inference is a vital skill for data analysts and researchers, as it helps determine cause-and-effect relationships in data. It remains highly relevant across industries such as healthcare, economics, and technology, especially with the increasing availability of large datasets and advanced statistical tools like R and Python. Professionals in this field are in demand for designing experiments, analyzing observational data, and informing decision-making processes.

What skills and qualifications are needed for a causal inference position?

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 jobs use causal inference?

Causal inference is used in various roles such as data scientist, epidemiologist, econometrician, and policy analyst. These jobs involve analyzing data to determine cause-and-effect relationships, often using statistical tools and programming languages like R or Python. Professionals in these fields work in industries like healthcare, finance, government, and technology to inform decision-making and policy development.

What are 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?

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 Cambridge, MA? The most popular types of Causal Inference jobs in Cambridge, MA are:
What are popular job titles related to Causal Inference jobs in Cambridge, MA? For Causal Inference jobs in Cambridge, MA, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Cambridge, MA look for? The top searched job categories for Causal Inference jobs in Cambridge, MA are:
Causal Inference jobs near you
Infographic showing various Causal Inference job openings in Cambridge, MA as of August 2026, with employment types broken down into 78% Full Time, 18% Part Time, and 4% Contract. Highlights an 68% Physical, 2% Hybrid, and 30% Remote job distribution, with an average salary of $108,457 per year, or $52.1 per hour.

Postdoctoral Research Position in Causal Inference

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Re-posted 27 days ago

Harvard University rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

80th of 616 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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