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Causal Inference Machine Learning Postdoctoral Jobs in Berkeley, CA

Neura Market

Economist

San Francisco, CA · On-site

Familiarity with causal inference, machine learning methods, or structural modeling. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial ...

Attentive

Head of Applied Data Science

San Francisco, CA · On-site

$320K - $380K/yr

Drive the development of statistical models, machine learning solutions, experimentation frameworks, and causal inference methodologies to improve product performance and customer outcomes

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Berkeley, CA

Causal Inference Machine Learning Postdoctoral information

See Berkeley, CA salary details

$43.5K

$66.4K

$74.7K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Sep 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Berkeley, CA is $66,393.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,500.00 and $69,200.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Berkeley, CA?

For Causal Inference Machine Learning Postdoctoral jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Berkeley, CA look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Berkeley, CA with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Berkeley, CA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 20% Part Time, and 3% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $66,393 per year, or $31.9 per hour.

Member of Research Staff, Causal Inference, Voleon Securities

Berkeley, CA • On-site

The Voleon Group
Investment Management and Consulting Services • 11 - 50 employees
Learn more about The Voleon Groupexternal

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 28 days ago

Job description

Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type

Full time

Location Type

Hybrid

Department

Securities

Compensation

The listed base salary range for this position is based upon the location(s) of this posting. Individual salaries are determined through a variety of factors, including, but not limited to, education, experience, knowledge, skills, and geography. Base salary does not include other forms of total compensation such as bonus compensation and other benefits.

Our benefits package includes medical, dental, and vision coverage, life and AD&D insurance, 20 days of paid time off, 9 sick days, and a 401(k) plan with a company match.

Voleon Securities, a new business within the Voleon Group, provides liquidity in securities markets. We apply state-of-the-art AI/ML techniques to construct our liquidity-provision strategies. For more than a decade, our affiliate Voleon Capital Management has led the hedge fund industry and worked at the frontier of applying AI/ML to investment management, becoming a multibillion-dollar asset manager. Voleon Securities builds on Voleon's deep real-world experience applying ML to financial markets.

We are looking to add an experienced and creative causal inference researcher to our growing ML research group. We welcome researchers with both strong theoretical foundations and experience applying causal inference methods in industrial settings. Financial applications of causal inference are challenging and demand new methods that go beyond the academic state-of-the-art; it is critical candidates have a deep understanding of the field to draw inspiration for new methodology.

This is a chance to join the initial buildout of a fully modern securities business rooted in the frontier of AI/ML and statistics. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals.

As a Member of Research Staff, you will work at the forefront of modern statistical machine learning. Your research colleagues across Voleon have collectively published hundreds of academic articles in top-tier venues on machine learning, systems, and theory, and we meet regularly to stay current on the latest academic research and share ideas. Founded in 2007 by two leading scientists, Voleon supports a culture of curiosity, collegiality, and creativity.

Your work will focus on financial market prediction and portfolio optimization. The behavior of financial markets is noisy and violates a number of classical statistical assumptions, and we've spent over a decade pioneering scientific advances in the application of machine learning techniques to this domain. You will work with a complex and diverse array of datasets to implement and iterate on predictive models. Predicting financial markets is an enduringly hard problem, but results are immediate and unambiguous.

Years of academic training has prepared you for this moment. You won't just conduct research, you'll apply it on a daily basis, working with a team across the entire life cycle of applied research problems. Your work will span from basic research to productizing solutions and validating their efficacy in live trading.

Relocation and work visa eligibility for qualified candidates

Responsibilities

Develop a rich understanding of Voleon's challenges and methodologies and propose causal inference research innovations and experiments to build, maintain and optimize models of the market

Prepare and analyze new market datasets to gain insight into market microstructure

Develop, validate, and implement improvements to our models of the market

Design and conduct synthetic and live trading experiments to sharpen understanding of market behavior

Communicate and collaborate effectively with other Members of Research Staff and Software Engineers at each stage, driving progress towards tangible outcomes

Keep up to date on the latest causal inference academic research to identify novel approaches to explore for application to our domain

Requirements

Ph.D. level coursework is required, and a Ph.D. degree in a relevant field is preferred

Background in causal inference and statistics with a strong track record of publishing causal inference papers in top tier journals and conferences

Evidence of strong mathematical abilities (e.g., publication record, graduate coursework, or competition placement)

Interest in software development techniques and willingness to write production-level code (Python)

Eagerness to work in a fast paced and growing business

Interest in financial applications is essential, but prior finance industry experience is not a pre-requisite

Equal Opportunity Employer

The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

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About Voleon Group

Sourced by ZipRecruiter

Industry

Investment management and consulting services

Company size

11 - 50 Employees

Headquarters location

Berkeley, CA, US

Year founded

2007

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