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Remote Bayesian Jobs (NOW HIRING)

Numerator

Sr. Data Scientist II

Numerator is seeking a Sr. Data Scientist II (Bayesian Modeling) to help build, enhance, and scale ... CPG / FMCG / retail experience, or work with user-level purchase or panel data #LI-Remote There is ...

Airbnb

Senior Data Scientist, Trust (Inference)

Modeling : Build and iterate on statistical and Bayesian models that quantify risk, estimate ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

Federato Technologies

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

This is a remote position, but we do have an office in San Fransisco. You will be the first data ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

This is a remote position, but we do have an office in San Fransisco. You will be the first data ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Airbnb

Senior Data Scientist, Trust (Inference)

Modeling : Build and iterate on statistical and Bayesian models that quantify risk, estimate ... This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or ...

Splice

Staff Data Analyst (Remote US)

$160K - $200K/yr

REMOTE THE ROLE: As a Staff Data Analyst, you will play a central role in shaping the future of ... An understanding of Bayesian Inference * Experience building and managing a data team roadmap

OneStudyTeam

Senior Data Scientist

Boston, MA · On-site +1

$140K - $190K/yr

Create and refine predictive models (Bayesian inference, regression analysis, time-series ... LI-Remote We value diversity and believe the unique contributions each of us brings drives our ...

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How much do remote bayesian jobs pay per year?

As of Sep 15, 2026, the average yearly pay for remote bayesian in the United States is $127,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,000.00 and $143,500.00 per year, depending on experience, location, and employer.

What is a Remote Bayesian?

A Remote Bayesian is a professional who specializes in Bayesian statistics and probabilistic modeling while working remotely, often in fields like data science, machine learning, or research. They use Bayesian methods to update probabilities and make predictions based on data, collaborating with teams through digital communication tools. Remote Bayesians may work for tech companies, research institutions, or as independent consultants, applying their expertise to solve complex problems without being tied to a physical office location.

How do Remote Bayesian professionals typically collaborate with cross-functional teams given the virtual nature of their work?

Remote Bayesian professionals often work closely with data scientists, engineers, and decision-makers through virtual collaboration tools such as video conferencing, shared code repositories, and project management platforms. Clear communication is key, as they must explain complex probabilistic models and inferences to both technical and non-technical stakeholders. Regular check-ins and documentation help ensure alignment on project goals, data requirements, and model outcomes. This collaborative dynamic fosters an environment where insights from Bayesian analysis can directly inform business or research decisions, despite the physical distance.

What are the key skills and qualifications needed to thrive as a Remote Bayesian, and why are they important?

To thrive as a Remote Bayesian, you need strong statistical knowledge, expertise in Bayesian inference, and a background in mathematics or data science, often supported by an advanced degree. Familiarity with programming languages like Python or R, Bayesian software such as Stan or PyMC, and experience with remote collaboration tools are typically required. Critical thinking, problem-solving, and clear communication are essential soft skills for interpreting results and working with distributed teams. These abilities are vital for delivering accurate, actionable insights in a remote environment where clear analysis and collaboration drive project success.

What is the difference between Remote Bayesian vs Remote Data Scientist?

AspectRemote BayesianRemote Data Scientist
Required CredentialsBackground in statistics, Bayesian methods, programming (Python/R)Statistics, computer science, or related degree; programming skills
Work EnvironmentResearch-focused, analytical tasks, often in tech or financeData analysis, modeling, business insights across industries
Industry UsageResearch institutions, AI, machine learning, financeTech companies, consulting, finance, healthcare

Remote Bayesian specialists focus on Bayesian statistical methods and probabilistic modeling, often in research or AI contexts. Remote Data Scientists have broader roles in data analysis and modeling across various industries. While both roles require strong analytical skills and programming, Remote Bayesian roles emphasize Bayesian techniques, whereas Remote Data Scientist roles encompass a wider range of data analysis tasks.

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Remote Bayesian jobs near you
Infographic showing various Remote Bayesian job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 62% Physical, 2% Hybrid, and 36% Remote job distribution, with an average salary of $127,031 per year, or $61.1 per hour.

Remote PhD Data Science Intern - Media Mix Modeling

CA • Remote

FocusKPI Inc.
Computing Infrastructure Providers, Data Processing, Web Hosting • 51 - 200 employees
Learn more about FocusKPI Inc.external

Other

Posted 4 days ago

Job description

Duration: 3 months
Employment: Full-time, Paid Internship
Compensation: Based on experience
Location: Remote
Company: FocusKPI
About the Role
FocusKPI is seeking a highly motivated PhD Data Science Intern to join our team for a three-month, full-time engagement focused on the research, development, and advancement of Media Mix Modeling (MMM) algorithms.
This is a hands-on, research-oriented role for someone with a strong foundation in statistics, econometrics, economics, or a closely related quantitative discipline who is interested in applying rigorous statistical methodology to real-world marketing and business problems.
The ideal candidate will have deep theoretical knowledge combined with practical experience developing statistical models end-to-end—from problem formulation and data preparation through model development, validation, interpretation, and production implementation.
The intern will work closely with senior data scientists and leadership to evaluate and enhance our MMM methodology, explore new modeling approaches, and translate advanced statistical techniques into scalable analytical solutions.
What You Will Do
  • Research and evaluate statistical and econometric approaches for Media Mix Modeling and marketing effectiveness measurement
  • Develop, test, and enhance MMM algorithms across the full modeling lifecycle
  • Work with time-series, panel, and observational marketing data to develop robust models of media response and business outcomes
Explore methodologies for:
    • Media response curves and saturation effects
    • Adstock and carryover effects
    • Incrementality and causal inference
    • Channel interaction and synergies
    • Seasonality, trends, and external factors
    • Model regularization and variable selection
    • Uncertainty estimation and statistical inference
    • Bayesian and frequentist modeling approaches
  • Develop model diagnostics and validation frameworks to assess model stability, predictive performance, statistical significance, and business interpretability
  • Conduct simulation and experimentation to understand algorithm behavior under different data-generating conditions
  • Compare alternative modeling methodologies and identify opportunities to improve model accuracy, robustness, and interpretability
  • Translate research findings into production-ready algorithms and analytical workflows
  • Work with real client datasets and understand the practical challenges of applying MMM to imperfect business data
  • Collaborate with senior data scientists to document methodology, assumptions, limitations, and results
  • Contribute to the development of next-generation MMM capabilities within FocusKPI
Required Qualifications
  • PhD in Statistics, Economics, Econometrics, Applied Mathematics, Data Science, or a closely related quantitative field
Strong theoretical foundation in:
    • Statistical modeling
    • Econometrics
    • Regression and multivariate analysis
    • Time-series analysis
    • Probability and statistical inference
    • Optimization
  • Strong understanding of causal inference and observational data
Demonstrated ability to develop statistical models end-to-end, including:
    • Problem formulation
    • Data preparation and feature engineering
    • Model specification
    • Estimation
    • Model diagnostics
    • Validation
    • Interpretation
    • Implementation
  • Strong programming skills in Python
  • Experience working with large, complex datasets
  • Ability to translate mathematical and statistical concepts into practical algorithms
  • Strong analytical and problem-solving skills
  • Ability to work independently while collaborating closely with senior technical team members
Preferred Qualifications
  • Direct experience with Media Mix Modeling (MMM)
  • Experience with marketing measurement, marketing analytics, or advertising data
  • Experience with Bayesian hierarchical models
  • Experience with causal inference, experimentation, or uplift modeling
  • Experience with time-series econometrics
Familiarity with:
    • Bayesian inference / MCMC
    • State-space models
    • Regularization
    • Constrained optimization
    • Nonlinear regression
    • Response curve estimation
    • Monte Carlo simulation
  • Experience with modern statistical computing frameworks such as PyMC, Stan, NumPyro, JAX, scikit-learn, statsmodels, or equivalent
  • Experience taking research concepts and converting them into reusable production code

NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.

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About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

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