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Mid Level Bayesian Statistics Jobs in California

Ohalo

Quantitative Geneticist

San Francisco, CA · On-site

... • Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy ... with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and ...

Ohalo

Quantitative Geneticist

San Francisco, CA · On-site +1

Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy, Pandas ... Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical ...

Ohalo

Quantitative Geneticist

South San Francisco, CA · On-site

Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy, Pandas ... Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical ...

Ohalo

Quantitative Geneticist

South San Francisco, CA · On-site +1

Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy, Pandas ... Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical ...

Prodege, LLC

Senior Data Scientist

El Segundo, CA · On-site

... level insights, segmentation frameworks, and behavioral analysis that enable smarter targeting ... statistical experimentation frameworks (Bayesian and frequentist) and tooling. • Partner with ...

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Mid Level Bayesian Statistics information

What can you do with Bayesian statistics?

A mid-level Bayesian statistics professional can apply Bayesian methods to model uncertainty, update probabilities with new data, and improve decision-making processes across various fields such as healthcare, finance, and technology. Proficiency in statistical software like R or Python and understanding of probabilistic programming are essential for implementing these techniques effectively.

Is Bayesian statistics difficult?

Bayesian statistics as a mid-level role involves understanding probability models, prior and posterior distributions, and using tools like R or Python for analysis. While it requires a solid grasp of statistical concepts and programming skills, the difficulty depends on prior experience with mathematics and data analysis. Consistent practice and formal training can help develop proficiency in this specialized area.

What job should I do if I like statistics?

A mid-level Bayesian statistician typically works in data analysis, research, or modeling roles across industries such as healthcare, finance, or technology. These roles involve applying statistical methods, programming skills in languages like R or Python, and understanding Bayesian inference to solve complex problems. Certification or advanced degrees in statistics or related fields can enhance job prospects.

What is the difference between Mid Level Bayesian Statistics vs Data Scientist?

AspectMid Level Bayesian StatisticsData Scientist
Required CredentialsMaster's or PhD in Statistics, Mathematics, or related fieldBachelor's or higher in Data Science, Computer Science, or related field
Work EnvironmentResearch-focused, analytical, often in finance, healthcare, or academiaCross-functional teams, data analysis, machine learning, business insights
Industry UsageStatistical modeling, probabilistic analysis, research projectsData analysis, predictive modeling, data visualization

Mid Level Bayesian Statistics specialists focus on advanced probabilistic modeling and statistical inference, often in research or specialized industries. Data Scientists have a broader scope, combining statistical analysis with programming and machine learning to solve business problems. While both roles require strong analytical skills, Bayesian statisticians typically emphasize probabilistic models, whereas Data Scientists integrate multiple techniques for data-driven decision-making.

What type of statistician makes the most money?

Senior-level statisticians, such as data science managers or quantitative research directors, tend to earn the highest salaries. Specializations in machine learning, Bayesian methods, or roles in finance and technology often command higher compensation, especially with advanced skills and relevant certifications.
What are the most commonly searched types of Bayesian Statistics jobs in California? The most popular types of Bayesian Statistics jobs in California are:
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Mid Level Bayesian Statistics jobs near you
Bayesian Statistics Expert - Problem Designer

Bayesian Statistics Expert - Problem Designer

Mercor

San Francisco, CA • Remote

$70 - $100/hr

Full-time

Posted 4 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Computational Bayesian Statistics and Applied Mathematics Expert
Type: Contract
Compensation: $70–$100/hour
Location: Remote
Commitment: 15–20 hours/week

Role Responsibilities

  • Design challenging computational problems to test AI capabilities in solving scientific and engineering tasks.
  • Develop problems requiring the use of specialized scientific software for simulations and experiment design.
  • Refine problems through iterative testing against state-of-the-art AI models to achieve target difficulty.
  • Collaborate on creating tasks that require strategic thinking and efficient information extraction.
  • Work independently to enhance problem designs based on feedback and model performance.

Qualifications

Must-Have

  • Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
  • Proven proficiency with at least one scientific software library (PyMC, FEniCS, etc.).
  • Strong Python skills for writing problem setups and solution validators.
  • Ability to work independently and refine problem designs.
  • Comfortable in a Linux/terminal environment with remote compute sandboxes.
  • Available for at least 15–20 hours/week.

Preferred

  • Experience across multiple domains or tools.
  • Familiarity with benchmark or evaluation design.
  • Background in scientific teaching or exam/problem-set design.
  • Experience with computational reproducibility and containerized environments.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.