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 ...
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 ...
Member of Research Staff, Causal Inference, Voleon Securities
New York, NY · On-site +1
$250K - $275K/yr
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 ...
Member of Research Staff, Causal Inference, Voleon Securities
New York, NY · On-site +1
$250K - $275K/yr
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 ...
The position emphasizes methodological innovation in modern causal inference, while also engaging with applications in areas such as infectious diseases, environmental health, and public health that ...
The position emphasizes methodological innovation in modern causal inference, while also engaging with applications in areas such as infectious diseases, environmental health, and public health that ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
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 ...
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 ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
We are looking for a Data Scientist with strong expertise in statistical inference and causal analysis to develop measurement frameworks for enterprise solutions. This role involves designing ...
We are looking for a Data Scientist with strong expertise in statistical inference and causal analysis to develop measurement frameworks for enterprise solutions. This role involves designing ...
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 ...
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 ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels. Responsibilities: * Deliver ...
Staff Data Scientist - Experimentation & Causal Inference
$163K - $220K/yr
About the Role: We're hiring our first Staff Data Scientist, Experimentation & Causal Inference to define how HighLevel learns from experiments and turns results into trustworthy product decisions ...
Staff Data Scientist - Experimentation & Causal Inference
$163K - $220K/yr
About the Role: We're hiring our first Staff Data Scientist, Experimentation & Causal Inference to define how HighLevel learns from experiments and turns results into trustworthy product decisions ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference
Manhattan, NY · On-site
$325/day
Own causal inference and experimentation standards across Headway. Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including ...
Senior Staff Data Scientist - Bayesian Experimentation & Causal Inference
Manhattan, NY · On-site
$325/day
Own causal inference and experimentation standards across Headway. Define the canonical approaches, guardrails, documentation, and review mechanisms for experiments and quasi-experiments, including ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, instrumental variables) * Experience with applied data ...
Causal Inference information
See salary details
$55K - $62.3K
0% of jobs
$62.3K - $69.6K
1% of jobs
$69.6K - $77K
3% of jobs
$77K - $84.3K
17% of jobs
$85.8K is the 25th percentile. Wages below this are outliers.
$84.3K - $91.6K
18% of jobs
The median wage is $96.2K / yr.
$91.6K - $98.9K
17% of jobs
$98.9K - $106.2K
18% of jobs
$106.5K is the 75th percentile. Wages above this are outliers.
$106.2K - $113.5K
17% of jobs
$113.5K - $120.9K
6% of jobs
$120.9K - $128.2K
1% of jobs
$128.2K - $135.5K
1% of jobs
$55K
$99.2K
$135.5K
How much do causal inference jobs pay per year?
As of Sep 10, 2026, the average yearly pay for causal inference in the United States is $99,231.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $108,500.00 per year, depending on experience, location, and employer.
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 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 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.
More about Causal Inference jobs
What cities are hiring for Causal Inference jobs?
Cities with the most Causal Inference job openings:
What are the most commonly searched types of Causal Inference jobs?
The most popular types of Causal Inference jobs are:
What states have the most Causal Inference jobs?
States with the most job openings for Causal Inference jobs include:
What job categories do people searching Causal Inference jobs look for?
The top searched job categories for Causal Inference jobs are:
- Computational Research
- Climate Research Scientist Machine Learning
- Overnight Computational Social Scientist
- Research Translation
- Postdoctoral In Plant Genomics
- Evening Data Scientist Experimentation
- Remote Variant Curation Scientist
- Data Scientist Fellow
- Connectomics
- Entry Level Postdoctoral Research Computational Chemistry
What other helpful pages are available for Causal Inference?
Other pages related to Causal Inference:
Member of Research Staff, Causal Inference, Voleon Securities
Berkeley, CA • On-site
The Voleon Group •
Investment Management and Consulting Services • 11 - 50 employees11 - 50 employees
Other
Medical, Dental, Vision, Life, Retirement, PTO
Posted 23 days ago
Job description
Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type
#J-18808-LjbffrFull time
Location TypeHybrid
DepartmentSecurities
CompensationThe 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
ResponsibilitiesDevelop 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
RequirementsPh.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 EmployerThe 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.
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