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Probabilistic Programming Bayesian Jobs in California

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 ...

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 ...

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

Familiarity with probabilistic programming or Bayesian methods for demand sensing * Experience with cloud ML infrastructure (AWS SageMaker, GCP Vertex, or equivalent) * Domain experience in energy ...

Engineer VII

Poway, CA · On-site

$128K - $229K/yr

We have an exciting opportunity for a Project Engineer integrated product team (IPT) leader to join ... Strong background in probabilistic methods (e.g., Bayesian inference, filtering, estimation theory)

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Probabilistic Programming Bayesian information

What are the typical challenges faced by professionals working in Probabilistic Programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

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

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What are popular job titles related to Probabilistic Programming Bayesian jobs in California? For Probabilistic Programming Bayesian jobs in California, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in California look for? The top searched job categories for Probabilistic Programming Bayesian jobs in California are:
What cities in California are hiring for Probabilistic Programming Bayesian jobs? Cities in California with the most Probabilistic Programming Bayesian job openings:
Infographic showing various Probabilistic Programming Bayesian job openings in California as of May 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 100% In-person job distribution.
Data Scientist II

Data Scientist II

Fabletics

El Segundo, CA • On-site

Full-time

Medical, Life, Retirement, PTO

Posted 21 days ago


Fabletics rating

6.9

Company rating: 6.9 out of 10

Based on 24 frontline employees who took The Breakroom Quiz


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 base salary range for this position is from $110,000-$125,000. The range provided includes the base salary that Fabletics expects to pay for 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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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 an equal opportunity 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 on diversity 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.

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