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Bayesian Modeling Jobs in New York (NOW HIRING)

Develop systematic trading models across FX, commodities, fixed income, and equity markets * Alpha ... Bayesian inference-as well as techniques for dealing with errors that can occur, such as auto ...

Cohort analysis and survival/churn modeling (e.g., Cox, Kaplan-Meier, BG/NBD). * Bayesian methods and probabilistic/hierarchical forecasting. * Data warehouses (Snowflake, BigQuery, Databricks) and ...

Develop systematic trading models across FX, commodities, fixed income, and equity markets * Alpha ... Bayesian inference-as well as techniques for dealing with errors that can occur, such as auto ...

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Bayesian Modeling information

What is Bayesian modeling?

Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.

How does a Bayesian modeling specialist typically collaborate with cross-functional teams in a workplace setting?

Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.

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

To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.

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

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

Infographic showing various Bayesian Modeling job openings in New York as of September 2026, with employment types broken down into 1% Internship, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Global Banking & Markets-New York-Associate, Quantitative Engineering-10452362

New York, NY • On-site

Goldman Sachs, Inc.
Finance and Insurance • 10K+ employees

$150K - $189K/yr

Full-time

Re-posted 4 days ago


Goldman Sachs rating

7.8

Company rating: 7.8 out of 10

Based on 28 frontline employees who took The Breakroom Quiz


Job description

Job Duties: Associate, Quantitative Engineering with Goldman Sachs & Co. LLC in New York, New York. Develop, implement, and document scenarios comprised of a broad range of economic and financial variables for businesses within the Firm.  Collaborate with internal stakeholders, analyzing user needs from a scenario design perspective and addressing data, model, and implementation issues. Analyze large data sets (structured and unstructured) to build predictive models of business-relevant market variables. Develop, refine, and improve scenarios by leveraging knowledge in financial markets, economics, current events, statistical analysis, and programming. Build and challenge risk models, identify and quantify vulnerabilities across market, credit, liquidity risk and modeling. Create and maintain clear and complete technical documentation of the risk-model performance testing approach and process.

Job Requirements: Master's degree (U.S. or foreign equivalent) in Financial Engineering, Financial Economics, Applied Mathematics, Data Science, Operations Research, or a related field and one (1) year of experience in job offered or a related quantitative engineering role OR Bachelor's degree (U.S. or foreign equivalent) in Financial Engineering, Financial Economics, Applied Mathematics, Data Science, Operations Research, or a related field and two (2) years of experience in job offered or a related quantitative engineering role. Prior experience must include one (1) year of experience (with a Master's degree) OR two (2) years of experience (with a Bachelor's degree) with 5 of the 7 following skills: C++, Java, or Python; developing probability and pricing models utilizing financial mathematics principles, including stochastic calculus, no-arbitrage pricing theory, partial differential equations, multivariable calculus, linear algebra, numerical methods, optimization, probability, or random processes; quantitative analysis and model development using advanced econometric, statistical, and mathematical techniques, including Bayesian analysis, time series analysis, or machine learning algorithms; performing risk management or scenario-based analysis; developing quantitative risk analytics, including factor models; developing rigorous and scalable data management and analysis tools to provide risk oversight and support the investment process; statistics and data driven performance analysis, including Linear Regression or Time Series Analysis to measure performance.

Salary Range: Annual base salary for this New York, New York-based position is $150,000 - $189,000.

The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.


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About Goldman Sachs

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At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869