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Probabilistic Programming Bayesian Jobs in California

Samba

Data Scientist

San Francisco, CA · On-site

$150K - $185K/yr

... Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record ... Partner with Data Engineering to define data requirements, validate pipelines, and ensure model ...

Snowflake

Senior Data Scientist

Menlo Park, CA · On-site

$156K - $224K/yr

You will partner closely with Finance, Sales, Product, and Analytics Engineering to improve ... as hierarchical, Bayesian, probabilistic, deep learning, or state-space models. * Strong ...

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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:
Probabilistic Programming Bayesian jobs near you
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

Samba

San Francisco, CA • On-site

$150K - $185K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 25 days ago

Job description

Samba is a media intelligence company. We know what the world is watching, reading, and thinking about - in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it's the most interesting data asset on the planet, because it's the most culturally relevant. 

ABOUT THE ROLE

We are looking for a hands-on Data Scientist to own and deliver complex measurement science and modeling work at the core of our measurement and audience sciences products. 

The role requires a deep, first-principles understanding of data science and machine learning - not just the ability to apply libraries, but the ability to reason clearly about model behavior, articulate trade-offs between approaches, and make defensible methodological decisions under ambiguity. This is emphatically a coding role - you will spend the majority of your time writing production-quality Python, building and evaluating models on large-scale viewership and web data, and delivering end-to-end ML solutions.

You will work closely with Data Engineering, Product, and go-to-market teams.

WHAT YOU'LL DO

  • Write and own production-quality Python code end-to-end - well-structured, tested, documented, and built to last; PySpark proficiency is essential for working with Samba's billion-row viewership datasets

  • Design, build, and deploy measurement models and statistical frameworks that power Samba's campaign measurement, reach/frequency estimation, and cross-platform attribution products

  • Apply the right statistical and ML technique to the right problem - drawing from hierarchical models, Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record linkage - and clearly articulate the reasoning behind your choices

  • Build and evaluate multi-touch and multi-channel attribution models; apply Causal ML methods - counterfactual modeling, meta-learners (S-learner, T-learner, X-learner), and heterogeneous treatment effect estimation - to advertising and viewership measurement problems

  • Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable, scalable, and production-ready

  • Lead technical design reviews and contribute meaningfully to architecture decisions across the Data Science team

  • Mentor junior Data Scientists through code review, pairing, and structured technical feedback - raising the team's technical floor

  • Communicate measurement methodologies and findings clearly to technical and non-technical audiences, including senior leadership and external clients

WHO ARE YOU

  • 5-7 years of professional data science experience - hands-on, delivery-focused, and measurable in shipped models and production systems

  • Expert-level Python - clean, modular, testable, production-ready code is your standard, not your aspiration

  • Advanced PySpark and Databricks - comfortable building and optimizing data pipelines and ML workflows on billion-row datasets

  • Deep, first-principles command of statistics and ML - you can explain from the ground up how these models work and you apply this understanding to make better modeling decisions

  • Solid grasp of experimental design - A/B testing, randomization, power analysis, and the conditions under which observational causal inference is appropriate

  • Fluent in the full ML lifecycle: feature engineering, model evaluation, deployment pipelines, drift monitoring, and iterative improvement in production

  • Hands-on experience with uplift modeling, synthetic control, difference-in-differences, or propensity-based approaches applied to advertising or media outcomes

  • Strong ownership mindset - you drive projects independently and are comfortable owning your models from data exploration through production delivery, with minimal hand-holding.

  • Clear communicator - able to translate statistical reasoning and model behavior into language that drives decisions with product, engineering, and leadership

  • Experience with multi-touch attribution (MTA) or multi-channel attribution modeling - understanding of the limitations of rule-based approaches and the methodological trade-offs of data-driven alternatives

  • Hands-on experience with Causal ML methods - counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation - applied to advertising or media measurement outcomes

  • Direct exposure to TV or digital viewership data - ACR signals, STB data, viewership panels, or cross-platform measurement (linear + CTV/OTT)

  • Familiarity with the measurement

  • t vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC, GRP/TRP frameworks)

  • Advanced degree (MS or PhD) in Statistics, Mathematics, Computer Science, or a related quantitative field - or equivalent depth demonstrated through work

$150,000 - $185,000 a year
Samba expects to offer a base salary between $150,000 - $180,000 USD per year for roles to be performed in San Francisco or California; actual base salary offered will depend on various factors including but not limited to location, experience, and performance. Base salary is just one component of Samba's total compensation package for employees. Other rewards may include bonuses, short-term incentives and long-term incentives. In addition, Samba provides health insurance, wellness offerings, life and disability insurance, a retirement savings plan, paid holidays and paid time off (PTO), and other employee benefits.
Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
 
Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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