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Mid Level Bayesian Statistics Jobs in California

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

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

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 ...

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 ...

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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 are the most commonly searched types of Bayesian Statistics jobs in California? The most popular types of Bayesian Statistics jobs in California are:
What are popular job titles related to Mid Level Bayesian Statistics jobs in California? For Mid Level Bayesian Statistics jobs in California, the most frequently searched job titles are:
What job categories do people searching Mid Level Bayesian Statistics jobs in California look for? The top searched job categories for Mid Level Bayesian Statistics jobs in California are:
What cities in California are hiring for Mid Level Bayesian Statistics jobs? Cities in California with the most Mid Level Bayesian Statistics job openings:
Mid Level Bayesian Statistics jobs near you
Principal Software Engineer - Circuit Simulation R&D

Principal Software Engineer - Circuit Simulation R&D

Cadence Design Systems Inc.

San Jose, CA • On-site, Remote

$136.50K - $253.50K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 23 days ago

Job description

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

We seek a graduate researcher-practitioner in applied mathematics/statistics to advance algorithms for electronic circuit simulation, Monte Carlo yield analysis, and optimization. You will work cross-functionally to turn deep math into production-grade technology.

Qualifications
  • Graduate degree in applied mathematics, statistics, or a closely related field (CS with strong math focus).
  • Demonstrated ability to conduct literature reviews, translate theory to practice, and deliver innovative results in real-world settings.
Core Expertise
  • Statistical inference: significance testing (p-values, confidence intervals), Bayesian statistics, design of experiments, Monte Carlo methods (random sampling, density estimation).
  • Rare-event and reliability analysis (a plus): importance sampling, subset simulation, cross-entropy methods, extreme value/tail modeling, yield estimation.
  • Surrogate modeling and Uncertainty Quantification (a plus): Gaussian processes, polynomial chaos, sparse grids, variance reduction.
Applied Mathematics (any of the following is a plus)
  • Optimization: linear, nonlinear, convex, integer, stochastic, variational; robust/multi-objective; derivative-free/global methods (e.g., CMA-ES, Bayesian optimization).
  • Numerical analysis: numerical linear algebra (sparse/Krylov/preconditioning), stiff ODE/DAE solvers, approximation, quadrature; model reduction (POD/MOR).
  • Differential equations: ODE/PDE/SDE, dynamical systems.
  • Probability and statistics: stochastic processes, inference, uncertainty quantification.
  • Data science: statistical learning, optimization for ML, dimensionality reduction.
Familiarity with Machine Learning (preferred)
  • Classical ML: regression (linear/logistic), regularization (ridge/lasso), classification (SVM, kNN), ensembles (trees, random forests, boosting).
  • Contemporary AI (a plus): graph neural networks, transformers, reinforcement/transfer learning, representation learning, active learning.
Software and Systems (Not needed but any of the following is aplus)
  • Programming proficiency in Python and/or C++ is a plus (NumPy/SciPy, PyTorch/JAX, performance optimization, clean APIs).
  • Strong computer science background is a plus (data structures, algorithms, version control, testing, CI/CD).
  • HPC/parallel computing (a plus): MPI, CUDA, distributed workflows.
Any prior Experience in the following areas is a plus
  • Scientific computing in one or more areas: computational electromagnetics, fluid/thermal/molecular dynamics, computational physics, or electrical circuit simulation.
  • Electronic design automation (EDA): SPICE/Spectre/Verilog-A, netlists, PVT/Monte Carlo flows, yield/parametric corners.
Responsibilities
  • Research, design, and validate algorithms for circuit simulation, rare-event estimation, and optimization.
  • Quantify accuracy/speed vs. baselines; perform rigorous statistical analyses.
  • Build robust, maintainable implementations and integrate with production toolchains.
  • Good Team Player as well as collaborate with cross-functional teams and document methods and results clearly.

The annual salary range for California is $136,500 to $253,500. You may also be eligible to receive incentive compensation: bonus, equity, and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the salary range is a guideline and compensation may vary based on factors such as qualifications, skill level, competencies and work location. Our benefits programs include: paid vacation and paid holidays, 401(k) plan with employer match, employee stock purchase plan, a variety of medical, dental and vision plan options, and more.

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