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Internship Bayesian Jobs (NOW HIRING)

ZF Group

Engineer I - Algorithm

Farmington Hills, MI · On-site

Master's preferred * 0-2 years of relevant experience; internships, co-ops, research assistantships ... Bayesian approaches) * Interest in convex optimization, optimal control, or model-predictive ...

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How much do internship bayesian jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for internship bayesian in the United States is $17.31, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What is an internship in Bayesian analysis?

An Internship in Bayesian analysis is a temporary, practical position focused on applying Bayesian statistical methods to real-world problems. Interns typically work under the supervision of experienced data scientists or statisticians, assisting with research, data modeling, and computational analysis using Bayesian techniques. These internships are valuable for students or recent graduates looking to gain hands-on experience in probabilistic modeling, data analysis, and statistical inference. Such internships often require a strong mathematical background and familiarity with programming languages like Python or R.

What are the key skills and qualifications needed to thrive as a Bayesian intern?

To thrive in a Bayesian Internship, you need a solid background in statistics, probability theory, and data analysis, typically supported by coursework or a degree in mathematics, statistics, or a related field. Familiarity with programming languages such as Python or R, and experience with statistical software and Bayesian modeling tools (e.g., Stan, PyMC) are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help interns interpret results and collaborate with research teams. These skills are essential for accurately applying Bayesian methods to real-world data and effectively communicating insights.

What are some common challenges interns face when working on Bayesian analysis projects, and how can they overcome them?

Interns working on Bayesian analysis projects often encounter challenges such as understanding complex statistical principles, learning new software (like Stan or PyMC), and interpreting probabilistic results. To overcome these obstacles, it's helpful to actively seek guidance from mentors, participate in team discussions, and utilize available learning resources. Collaborating closely with experienced team members and regularly reviewing project code and results can accelerate learning and help interns gain confidence in applying Bayesian methods to real-world problems.

What is the difference between Internship Bayesian vs Data Analyst Intern?

AspectInternship BayesianData Analyst Intern
Required CredentialsRelevant coursework in Bayesian statistics, basic programming skillsStatistics, data analysis, programming knowledge
Work EnvironmentResearch-focused, collaborative teams in tech or research firmsBusiness or tech companies, data-driven projects
Employer & Industry UsageUsed in research, AI, machine learning sectorsCommon in finance, marketing, tech industries
Search & Comparison IntentUnderstanding roles involving Bayesian methodsExploring data analysis internship opportunities

Internship Bayesian typically involves applying Bayesian statistical methods in research or AI projects, requiring knowledge of Bayesian theory and programming. Data Analyst Internships focus on analyzing datasets, creating reports, and supporting business decisions. While both roles involve data skills, Internship Bayesian emphasizes probabilistic modeling, whereas Data Analyst Internships focus on data visualization and reporting.

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Infographic showing various Internship Bayesian job openings in the United States as of September 2026, with employment types broken down into 16% Internship, 1% As Needed, 62% Full Time, 19% Part Time, 1% Temporary, and 1% Contract. Highlights an 74% Physical, 2% Hybrid, and 24% Remote job distribution, with an average salary of $35,995 per year, or $17.3 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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