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Bayesian Jobs in New York (NOW HIRING)

Berkley

Senior Data Scientist

Jersey City, NJ · On-site

Apply causal inference, Bayesian modeling, survival analysis, and simulation to solve high-stakes business problems. * Translate ambiguity to impact: Frame undefined problems with entrepreneurial ...

Applied Physics

Machine Learning Specialist

New York, NY · On-site

... Bayesian methods). * Experience in the broad application of one or more higher-level programming languages such as Python, Java, Scala, or C/C++. * Experience with one or more deep learning libraries ...

Point72

Quantitative Researcher - Macro

New York, NY · On-site

$150K - $200K/yr

Thorough understanding of and comfort using a variety of regression techniques-including OLS, MLS, Ridge, Lasso, and Bayesian inference-as well as techniques for dealing with errors that can occur ...

Applied Physics

Machine Learning Specialist

New York, NY · On-site

... Bayesian methods). * Experience in the broad application of one or more higher-level programming languages such as Python, Java, Scala, or C/C++. * Experience with one or more deep learning libraries ...

Applied Physics

Machine Learning Specialist

New York, NY · On-site

... Bayesian methods). * Experience in the broad application of one or more higher-level programming languages such as Python, Java, Scala, or C/C++. * Experience with one or more deep learning libraries ...

Point72

Quantitative Researcher - Macro

New York, NY · On-site

$150K - $200K/yr

Thorough understanding of and comfort using a variety of regression techniques-including OLS, MLS, Ridge, Lasso, and Bayesian inference-as well as techniques for dealing with errors that can occur ...

Scale

Sr Software Engineer

Manhattan, NY · On-site

$134K - $177K/yr

... Bayesian model fitting, DSP, control systems • Constraint modeling frameworks (Pyomo) and commercial/open-source solvers (HiGHS, Gurobi, GLPK) • FastAPI and microservices experience • React for ...

Incedo Inc

Senior Data Scientist

Manhattan, NY · On-site

Bayesian methods and probabilistic/hierarchical forecasting. * Data warehouses (Snowflake, BigQuery, Databricks) and BI tools (Tableau, Looker, Power BI). * dbt, version control (git), and ...

Scale Microgrid Solutions

Sr Software Engineer

New York, NY · On-site

$134K - $176K/yr

Experience with complex algorithm-driven problems: convex/constraint-based optimization problems, statistical modelling including Bayesian model fitting, DSP, control systems * Constraint modeling ...

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Bayesian information

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$163.4K

$174.9K

$187.5K

How much do bayesian jobs pay per year?

As of Sep 7, 2026, the average yearly pay for bayesian in New York is $174,923.00, according to ZipRecruiter salary data. Most workers in this role earn between $169,457.00 and $180,389.00 per year, depending on experience, location, and employer.

What is a Bayesian?

A Bayesian job typically involves applying Bayesian statistics, probabilistic modeling, and inference techniques to analyze data and make decisions under uncertainty. Professionals in this field use Bayes' theorem to update beliefs based on new evidence, often working in areas like machine learning, finance, healthcare, and research. Common roles include Bayesian statisticians, data scientists, and researchers who build probabilistic models to improve predictions and decision-making.

What are the typical projects or challenges faced in a Bayesian role?

In a Bayesian role, you’ll often work on projects involving probabilistic modeling, uncertainty quantification, and predictive analytics for real-world decision-making. Common challenges include structuring prior distributions, ensuring computational efficiency for complex models, and clearly explaining Bayesian results to non-technical stakeholders. You might collaborate closely with data engineers, domain experts, and business analysts to refine models and translate findings into actionable recommendations. This role offers the opportunity to tackle diverse analytical problems across industries like healthcare, finance, or tech, supporting ongoing professional growth and learning.

What are the key skills and qualifications needed to thrive in a Bayesian role, and why are they important?

To thrive as a Bayesian (typically a Bayesian Data Scientist or Statistician), you need a strong background in probability theory, statistical modeling, and mathematics, often with an advanced degree in statistics, data science, or a related quantitative field. Experience with programming languages such as Python or R, Bayesian analysis libraries (e.g., Stan, PyMC), and familiarity with statistical software are commonly required. Analytical thinking, collaborative teamwork, and the ability to communicate complex results clearly are valuable soft skills in this role. These abilities are essential for designing robust models, interpreting data accurately, and delivering actionable insights to interdisciplinary teams.

What are the most commonly searched types of Bayesian jobs in New York?

The most popular types of Bayesian jobs in New York are:

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

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

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

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

What cities in New York are hiring for Bayesian jobs?

Cities in New York with the most Bayesian job openings:

Bayesian jobs near you
Infographic showing various Bayesian job openings in New York as of August 2026, with employment types broken down into 80% Full Time, 18% Part Time, and 2% Contract. Highlights an 59% Physical, 3% Hybrid, and 38% Remote job distribution, with an average salary of $174,923 per year, or $84.1 per hour.

Senior Data Scientist

Berkley

Jersey City, NJ • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 25 days ago

Job description

Company Details
Driven by a commitment to collaboration, DNA partners with our customers and Operating Units by providing comprehensive solutions that not only address the challenge at hand, but proactively plan for the "What's Next" in our industry and beyond. Our mission is to drive transformation and provide exceptional capabilities and service to the operating units. DNA Enterprise Reporting generates meaningful and measurable value by delivering insights for our customers, partners, and shareholders using data and analytics.
Our vision is to enable operating unit profit and growth objectives by designing and delivering scalable solutions. With a culture centered on innovation and service stewardship, DNA stands as a community of leaders with eyes toward the future - leaders who truly care about growing not only their team members, but themselves, and take pride in their employees who shine. DNA offers endless ways to get involved and have the chance to grow your career into a wide range of roles. Come join us as we push forward into the future of industry leading technology and service solutions.
Company URL: https://www.berkley.com/
The company is an equal opportunity employer.
Responsibilities
We are seeking an exceptional Senior Data Scientist who is part deep technologist, part entrepreneur, and part strategic innovator. This is not a traditional analytics role, it is built for a builder. You will own the full lifecycle of high-impact AI/ML solutions, from whiteboard to production, writing substantial code and driving rigorous analysis that directly shapes enterprise decisions.
Sitting at the intersection of advanced machine learning, software engineering, and business strategy, you will architect and ship production-grade AI systems across underwriting, claims, operations, and finance.
Key Responsibilities:
AI Engineering & Production ML Development
  • Own the code, not just the model: Design, write, test, and deploy production-grade ML and AI systems using Python, modern ML frameworks, and cloud-native tooling.
  • Build generative AI & LLM-powered solutions: Architect and implement RAG pipelines, fine-tuning workflows, agentic systems, and LLM evaluation harnesses.
  • Engineer scalable ML pipelines: Develop robust feature engineering, training, inference, and monitoring pipelines built for reliability and scale.
  • Ship end-to-end: Take models from prototype through CI/CD into monitored production environments, including automated retraining and drift detection.

Advanced Data Science & Analytical Rigor
  • Lead complex analytical investigations: Apply causal inference, Bayesian modeling, survival analysis, and simulation to solve high-stakes business problems.
  • Translate ambiguity to impact: Frame undefined problems with entrepreneurial clarity: define success metrics, scope solutions, and move from question to insight at speed.
  • Ensure reproducibility and rigor: Establish standards for experiment tracking, version control, and model validation aligned with enterprise governance requirements.

Entrepreneurial Innovation & Strategic Influence
  • Rapidly prototype and validate: Move from idea to working proof-of-concept in days, not months using experimentation to de-risk investment before scaling.
  • Influence enterprise standards: Shape the organization's model development, validation, and deployment standards as a principal-level technical authority.

Qualifications
Education
  • Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a closely related quantitative field.
  • Master's or PhD preferred

Experience
  • 3-5+ years of hands-on experience in applied machine learning, data science, or AI engineering not just analytics. Demonstrated track record of shipping ML models and AI systems to production, including ownership of monitoring and maintenance.
  • Experience leading complex, end-to-end data science projects from problem definition through deployment and business impact measurement.
  • Proven ability to influence technical direction and strategy without direct management authority.

Technical Proficiency (Must Be Hands-On)
  • Python (expert-level): NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, Hugging Face, LangChain/LlamaIndex or equivalent.
  • ML Engineering: Feature stores, model registries (MLflow), experiment tracking, CI/CD for ML, containerization (Docker/Kubernetes).
  • LLMs & Generative AI: Prompt engineering, RAG architecture, fine-tuning, evaluation frameworks, and agentic workflow design.
  • SQL & Data Engineering: Complex query optimization, dbt or similar, working fluently with Spark or Databricks.
  • Cloud Platforms: Azure ML preferred; AWS SageMaker or GCP Vertex AI experience
  • Statistics & ML Foundations: Regression, classification, clustering, time-series, Bayesian methods, causal inference, and model interpretability (SHAP, LIME).
  • Software Engineering Practices: Git, code review, unit testing, design patterns you write code that others can maintain.

Preferred Qualification
  • Experience in financial services, insurance, or other regulated industries with model risk management requirements.
  • Contributions to open-source ML projects
  • Experience building and operating real-time inference systems (low-latency APIs, streaming prediction pipelines).
  • Familiarity with model governance frameworks and regulatory requirements
  • Experience with agentic AI systems, multi-modal models, or domain-adapted LLMs in an enterprise context.
  • Background in agile/product-oriented analytics teams with sprint-based delivery.

Additional Company Details
We do not accept any unsolicited resumes from external recruiting agencies or firms.
The company offers a competitive compensation plan and robust benefits package for full-time regular employees which for this role include:
• Base Salary Range: $150,000 - $200,000
• Eligible to participate in annual discretionary bonus.
• Benefits: Health, Dental, Vision, Life, Disability, Wellness, Paid Time Off, 401(k) and Profit-Sharing plans.
The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment.
Sponsorship Details
Sponsorship not Offered for this Role

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