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Bayesian Phd Jobs in New York (NOW HIRING)

Intuit

Staff AI Scientist

Manhattan, NY · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

New York, NY · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

Manhattan, NY · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Point72

Quantitative Researcher - Macro

New York, NY

$150K - $200K/yr

Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in ... Bayesian inference-as well as techniques for dealing with errors that can occur, such as auto ...

Point72

Quantitative Researcher - Macro

New York, NY · On-site

$150K - $200K/yr

Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in ... Bayesian inference-as well as techniques for dealing with errors that can occur, such as auto ...

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Bayesian Phd information

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 are the key skills and qualifications needed to thrive as a Bayesian PhD?

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 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 is the difference between Bayesian Phd vs Data Scientist?

AspectBayesian PhdData Scientist
Required CredentialsPhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, academic or specialized industry rolesBusiness-focused, tech companies, or consulting firms
Industry UsageAcademic research, advanced analytics, specialized modelingData analysis, machine learning, business insights
Common Search/ComparisonYesYes

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 are popular job titles related to Bayesian Phd jobs in New York?

For Bayesian Phd jobs in New York, the most frequently searched job titles are:

What job categories do people searching Bayesian Phd jobs in New York look for?

The top searched job categories for Bayesian Phd jobs in New York are:

What cities in New York are hiring for Bayesian Phd jobs?

Cities in New York with the most Bayesian Phd job openings:

Bayesian Phd jobs near you
Infographic showing various Bayesian Phd job openings in New York as of August 2026, with employment types broken down into 84% Full Time, 13% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.

Member of Technical Staff, Bayesian Statistics

Ataraxis AI

New York, NY • On-site

$100K - $300K/yr

Full-time

Posted 10 days ago

Job description

About Ataraxis AI
Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. Our goal is to make disease predictable. To accomplish this, we develop new AI methods that predict patient outcomes and treatment response, and build clinical tools to assist physicians in selecting the most optimal treatments for their patients.
Our AI research lab discovers and develops methods to recognize patterns and predict outcomes across complex, multi-modal clinical data. This spans our causality (Ataraxis™ Tau), foundation model (Falcon and Kestrel for digital pathology), and survival analysis research.
Our first clinical products, such as Ataraxis™ Breast for breast cancer, already help patients get the most appropriate treatment across the best academic institutions and community clinics worldwide.
At Ataraxis, you will have a unique opportunity to shape not only the future of our company, but also the future of healthcare. You will join an exceptional team at the forefront of clinical AI research and deployment. Our advisors include AI pioneers such as our founding advisor, Yann LeCun, and distinguished oncologists from top cancer research institutions, all united by the mission to redefine precision medicine.
Ataraxis has raised over $24 million in funding, including a $20 million Series A led by top venture capital funds such as Thiel Capital/Founders Fund (OpenAI, SpaceX, Palantir), Obvious Ventures (AMI Labs, Inceptive, Radical Numerics, Recursion), and AIX Ventures (Hugging Face, Perplexity).
We are an company with a flat organizational structure, where every team member is empowered to actively contribute. Leadership roles are earned by those who demonstrate initiative and consistently deliver exceptional results. Strong work ethic and the ability to prioritize ruthlessly are essential.
Responsibilities
  • Design and implement novel Bayesian statistics methods.
  • Translate machine learning papers into production-ready code.
  • Build robust model evaluation frameworks.
  • Disseminate the results by co-authoring research papers and abstracts.
  • Collaborate with a multidisciplinary team of engineers and scientists.
  • Co-mentor junior members of the team.
Qualifications
  • PhD degree in statistics or machine learning.
  • Excellent knowledge of Bayesian statistics, including Gaussian processes and Bayesian clinical trial design.
  • Passion for research, attention to detail and ability to drive tasks to completion. Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals.
  • Excellent understanding of core machine learning concepts.
  • Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
  • Excellent skills in Python and PyTorch.
  • Experience with applying Bayesian statistics to uncertainty quantification in deep learning and model explainability.
  • Experience in deep learning. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.

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