PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or ... learning, Bayesian optimization, probabilistic modeling, or statistical methods. * Hands-on ...
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PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or ... learning, Bayesian optimization, probabilistic modeling, or statistical methods. * Hands-on ...
Applied Data Scientist
Santa Rosa, CA · On-site
$117K - $196K/yr
Qualifications * Master's or PhD in Data Science, Computer Science, Electrical Engineering ... Familiarity with Bayesian Optimization and feature engineering for time-series or signal data
Applied Data Scientist
Santa Rosa, CA · On-site
$117K - $196K/yr
Qualifications * Master's or PhD in Data Science, Computer Science, Electrical Engineering ... Familiarity with Bayesian Optimization and feature engineering for time-series or signal data
Applied Data Scientist
Santa Rosa, CA · On-site
$117K - $196K/yr
Qualifications * Master's or PhD in Data Science, Computer Science, Electrical Engineering ... Familiarity with Bayesian Optimization and feature engineering for time-series or signal data
Applied Data Scientist
Santa Rosa, CA · On-site
$117K - $196K/yr
Qualifications * Master's or PhD in Data Science, Computer Science, Electrical Engineering ... Familiarity with Bayesian Optimization and feature engineering for time-series or signal data
Bayesian Phd information
What are some common challenges faced by a Bayesian PhD researcher during collaborative projects?
Bayesian PhD researchers often collaborate with interdisciplinary teams, which can present challenges such as communicating complex statistical concepts to non-specialists and integrating Bayesian methods with other analytical frameworks. Balancing the depth of theoretical work with practical problem-solving, managing computational demands, and aligning project goals with collaborators' expectations are also common hurdles. Successful collaboration typically requires strong communication skills, adaptability, and a willingness to bridge methodological gaps between disciplines.
What are the key skills and qualifications needed to thrive as a Bayesian PhD, and why are they important?
To thrive as a Bayesian PhD, you need advanced knowledge of probability theory, statistical inference, and mathematics, typically supported by a doctoral degree in statistics, mathematics, or a related field. Proficiency with statistical programming languages like R, Python, and specialized Bayesian tools such as Stan or BUGS is essential. Strong critical thinking, problem-solving, and clear communication skills help in articulating complex analyses and collaborating across disciplines. These capabilities are crucial for developing rigorous models, conducting impactful research, and translating statistical insights into actionable solutions.
What is the difference between Bayesian Phd vs Data Scientist?
| Aspect | Bayesian Phd | Data Scientist |
|---|---|---|
| Required Credentials | PhD in Statistics, Mathematics, or related field | Bachelor's or Master's in Data Science, Statistics, or related field |
| Work Environment | Research-focused, academic or specialized industry roles | Business-focused, tech companies, or consulting firms |
| Industry Usage | Academic research, advanced analytics, specialized modeling | Data analysis, machine learning, business insights |
| Common Search/Comparison | Yes | Yes |
While a Bayesian PhD specializes in advanced statistical modeling and research, a Data Scientist applies data analysis and machine learning techniques in practical business contexts. Both roles require strong analytical skills, but the Bayesian PhD typically focuses on theoretical development, whereas the Data Scientist emphasizes application and implementation.
What is a Bayesian PhD?
A Bayesian PhD typically refers to an individual who has completed a doctoral program with a focus on Bayesian statistics or Bayesian methods in their research. Bayesian statistics is a branch of statistics that uses probability distributions to represent uncertainty about unknowns, updating beliefs as new data becomes available. Students in this field learn to develop and apply Bayesian models to a wide range of problems in science, engineering, and social sciences. A PhD program with a Bayesian focus often involves advanced coursework in probability theory, statistical inference, and computational methods, as well as original research using Bayesian approaches.
What job categories do people searching Bayesian Phd jobs in Santa Rosa, CA look for? The top searched job categories for Bayesian Phd jobs in Santa Rosa, CA are:
What cities near Santa Rosa, CA are hiring for Bayesian Phd jobs? Cities near Santa Rosa, CA with the most Bayesian Phd job openings:
Contractor
Posted 11 days ago
Job description
Hello There,
My name is Himanshu Sharma, and I serve as the Recruitment Lead at Kanak-IT INC. I am reaching out to share an excellent career opportunity for the role of Machine Learning Engineer with our esteemed client. If you are interested then please share your updated resume at Himanshu01@kanakits.com .
Job Description
Title: Machine Learning Engineer
Location: South San Francisco, CA - hybrid role in Bay Arear
Position Type: Contract
Note: DO NOT SEND WITHOUT MOLECULAR EXPERIENCE,
Work on ML workflows for molecular property prediction & generative modeling to accelerate drug discovery. 3–5 yrs esp. or PhD with publications in molecular design.
Must have Masters or PH.D. Must have experience in working environment or while getting Master’s or no to very little work exp with PH.D in Molecular design. Need to have portfolio of their work or be published. Find me Machine Learning with Molecular experience in Bay Area or someone who will relocate as last resort.
MindSource is looking for a Machine Learning Engineer to join our client's team in South San Francisco, CA. They will be developing and deploying advanced computational methods for molecular design. This is a 12-month hybrid contract.
About the Role
- Build pipelines for probabilistic molecular property prediction and Bayesian acquisition to power active learning–driven drug discovery.
- Engineer workflows for molecular generative modeling and other innovative design approaches.
- Collaborate with machine learning scientists, engineers, computational chemists, and biologists.
- Partner with therapeutic development teams to analyze existing molecules and design new candidates.
- Contribute to ongoing initiatives while driving new research directions.
Qualifications
- PhD in Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, or related quantitative field — OR MS + 3+ years of relevant industry experience.
- Demonstrated expertise in production-ready ML workflows (e.g., PyTorch + Lightning + Weights & Biases).
- Strong track record of achievement (e.g., high-impact first-author publication or equivalent).
- Excellent written, visual, and verbal communication skills.
Preferred Experience
- Knowledge of physical modeling (e.g., molecular dynamics) and cheminformatics (e.g., RDKit).
- Background in molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, or statistical methods.
- Hands-on experience with Python, PyTorch, Torch Geometric, PyTorch Lightning, RDKit, and BoTorch.
- Public portfolio of computational projects (e.g., GitHub).