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Bayesian Modeling Jobs (NOW HIRING)

You bring expertise in Bayesian modeling and a strong statistical foundation, approaching ambiguous problems with structure. When faced with an unknown, you don't stop at the question; you identify ...

You bring expertise in Bayesian modeling and a strong statistical foundation, approaching ambiguous problems with structure. When faced with an unknown, you don't stop at the question; you identify ...

As a consequence you will apply and/or learn a wide variety of statistical techniques including time series analysis, high dimensional clustering, machine learning, data mining and Bayesian modeling.

As a consequence you will apply and/or learn a wide variety of statistical techniques including time series analysis, high dimensional clustering, machine learning, data mining and Bayesian modeling.

... Bayesian modeling--in non-stationary, adversarial environments. • Collaborate with product and engineering teams to deploy your models in production and run real-world experiments with rapid ...

Lead Bayesian Health's AI/ML organization with a hands-on, scrappy approach: setting technical vision, rolling up your sleeves on critical modeling work, and building a world-class team that ships ...

Data Transformation (dbt) Semantic Layers (Cube, Looker, dbt Metrics) TypeScript Bayesian modeling experience - ideally Marketing Mix Models (PyMC, Stan, or similar..). Understands priors, MCMC ...

Product Manager

$145K - $185K/yr

About Bayesian Health Bayesian Health is a clinical AI platform that empowers nurses and doctors to ... Collaborate with research to define data requirements for model training and ground truth for model ...

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How much do bayesian modeling jobs pay per hour?

As of May 30, 2026, the average hourly pay for bayesian modeling in the United States is $58.71, according to ZipRecruiter salary data. Most workers in this role earn between $52.64 and $68.27 per hour, depending on experience, location, and employer.

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

To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.

How does a Bayesian Modeling specialist typically collaborate with cross-functional teams in a workplace setting?

Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.

What is Bayesian modeling?

Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.

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Full-time

Medical, Life, Retirement, PTO

Posted 12 days ago


Job description

Job Description

Fabletics is looking for a Data Scientist to join our Marketing Science & Analytics team.

How do you fit in?

As a Data Scientist at Fabletics, you will play a key role in developing insights that empower our internal teams to scale marketing spend effectively through rigorous media measurement, from ad exposure to conversion. You will collaborate closely with Data Analytics, Media, and a diverse Data Science team with various expertise and focuses.

Tools and Technologies:

Expect to leverage Python and SQL daily, with larger projects involving Databricks and Airflow for scale and automation. Flexibility in choosing the best tools for your role is encouraged, reflecting our commitment to ongoing education and exploration of new technologies and modeling methods.

Ideal Candidate:

We seek a detail-oriented, solutions-focused Data Scientist with a deep understanding of the media measurement landscape and experience evaluating marketing effectiveness at scale from within a measurement vendor, media agency, or in-house marketing science team. You bring expertise in Bayesian modeling and a strong statistical foundation, approaching ambiguous problems with structure. When faced with an unknown, you don't stop at the question; you identify what you need to find out, articulate a clear path forward, and communicate your reasoning at every step. You take ownership of your projects end-to-end, proactively driving progress and keeping stakeholders informed without being asked. A natural collaborator and clear communicator, you are equally comfortable presenting your methodology to technical peers and explaining your findings to non-technical partners.

What you will be doing:

  • Incrementality Testing:

    • Design and execute rigorous incrementality and lift tests (including geo experiments, holdout tests, and A/B tests) to measure the true causal impact of marketing interventions

    • Apply Bayesian modeling and causal inference methods to analyze test results, quantify uncertainty, and produce actionable recommendations for marketing spend optimization

    • Own the end-to-end testing process, from proactively structuring experiment designs and clearly defining hypotheses and expected business impact, through execution, analysis, and stakeholder communication of results

  • Media Mix Modeling & Attribution:

    • Develop and maintain advanced media mix models and attribution systems to assess the impact of digital marketing channels and track touchpoints throughout the customer journey

    • Collaborate with cross-functional teams to align models with business goals and ensure comprehensive, accurate data inputs

  • Bayesian Modeling:

    • Design, develop, and maintain Bayesian statistical models as the primary analytical approach across marketing science projects, with a focus on quantifying uncertainty and informing decision-making

    • Apply probabilistic programming tools (PyMC preferred) to build and iterate on models; clearly document methodology, assumptions, and outputs for both technical and non-technical audiences

    • Regularly validate and refine models to ensure accuracy; proactively identify opportunities for optimization and propose data-driven solutions to enhance performance

  • Data Analysis and Visualization:

    • Utilize statistical and machine learning techniques to derive insights from large datasets

    • Translate findings into clear, compelling outputs (including dashboards, reports, and stakeholder presentations) and effectively communicate results to both technical and non-technical audiences across marketing, analytics, and technology teams

  • Collaboration and Communication:

    • Work closely with marketing, analytics, and technology teams to understand data requirements for both marketing and personalization initiatives

    • Clearly communicate complex findings and algorithmic recommendations to non-technical stakeholders

What you can bring:

  • 2-4 years of work experience as a Data Scientist

  • Fluent in R or Python (Python preferred), SQL, and Git

  • Proven experience in media mix modeling, media attribution, and incrementality testing, ideally gained within a measurement vendor, media agency, or sophisticated in-house marketing science function, with familiarity in how measurement solutions are scoped, delivered, and communicated to stakeholders

  • Hands-on experience designing and analyzing marketing experiments, including A/B tests, lift tests, and geo experiments; strong command of causal inference methods for interpreting results in real-world, non-ideal conditions

  • Strong foundation in statistical modeling, with hands-on experience in Bayesian methods and probabilistic programming (PyMC or equivalent)

  • The ability to understand and implement publications in the fields of Data Science/Machine Learning as applied to marketing

  • Strong communication skills with the ability to clearly articulate complex topics, project status, methodology, and next steps to both technical and non-technical audiences, including a structured approach to diagnosing and resolving blockers independently

  • Ideal candidate brings foundational AI literacy, including experience using generative AI tools, and a proactive mindset to experiment with new technologies to drive productivity and innovation.

Preferred Qualifications:

  • Advanced degree in Statistics, Computer Science, Econometrics, or related fields

  • Experience designing geo experiments or market-level tests for measuring marketing effectiveness at scale

  • Experience with data visualization tools such as Tableau

  • Familiarity with marketing tech stack and digital data technologies (Google Analytics, pixel tracking, etc.)

  • Experience in e-commerce, subscription models, and/or direct-to-consumer brands

Where we are:

  • This role will be based in our El Segundo Headquarters

Compensation & Total Rewards:

At Fabletics, we believe work and life should fit together!We continue to build a culture of flexibility, to empower you to do your best and put yourself first.Our Total Rewards program rewards employees for their hard work, supporting their health, well-being, families, and ultimately their life journey. Total Rewards at Fabletics includes:

-Hybrid Work Schedule*

-Discretionary Paid Time Off*

-Summer Fridays*

-Healthcare Plans

-Employee Discounts

-401k

-Annual Bonus Program

-Equity Program*

-And More

*Varied for retail, fulfillment and fully remote roles.

The annual basesalary range for this position is from $110,000-$125,000. The range provided includes the base salary that Fabletics expects topayfor the role. Offered base salary will be dependent on factors including the scope and complexity of the role, candidate's related work experience, subject matter expertise and work location.

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#LI-Fabletics

Security Alert: Protect yourself from scams

At Fabletics, we're dedicated to recruiting top talent who share our drive for innovation. To safeguard candidates, Fabletics emphasizes legitimate recruitment practices. Initial communication is primarily via official email addresses and LinkedIn; beware of deviations. Personal data and sensitive information will not be solicited during the application phase. Interviews are conducted via phone, in person, or through the approved platforms Teams or Zoom-never via messaging apps or other calling services. Offers are merit-based, communicated verbally, and followed up in writing. If personal information is requested to initiate the hiring process, rest assured it will be through secure and protected means.

Fabletics, Inc. is anequalopportunity employer. We recruit, employ, compensate,develop,and promote regardless of race,national origin,religion, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, and other protected status as required by applicable.At Fabletics, Inc., we champion a vibrant workplace culture that thrives ondiversity law and do not tolerate discrimination or harassment. We are one team from many backgrounds, innovating through diversity of individuals, who are driven by passion for creating an inclusive space for all.Fabletics, Inc. will continue to champion a workplace culture that prizes diversity and inclusivity.

We encourage you to apply regardless of meeting all qualifications and/or requirements.