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Postdoctoral In Bayesian Statistics Jobs in New York

Applicants should be recent PhD graduates in computer science, mathematics, statistics, economics ... Postdoctoral Associates at DIMACS are encouraged to collaborate with DIMACS members and visitors ...

Position Details Position Information Recruitment/Posting Title Postdoctoral Associate Job Category ... The candidate should be proficient in using statistical packages (SPSS, STATA, SAS etc.). Preferred ...

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Postdoctoral In Bayesian Statistics information

What is a Postdoctoral position in Bayesian Statistics?

A Postdoctoral position in Bayesian Statistics is a research-focused role for individuals who have recently completed their PhD in statistics, mathematics, or a related field. These positions involve conducting advanced research using Bayesian methods, which apply probability to infer statistical conclusions. Postdocs often work on developing new Bayesian models, collaborating on interdisciplinary projects, and publishing research findings. Such positions are typically temporary and designed to further prepare researchers for academic, industry, or governmental roles.

What are some common challenges faced by postdoctoral researchers in Bayesian statistics, and how can they be addressed?

Postdoctoral researchers in Bayesian statistics often encounter challenges such as managing complex, high-dimensional data, staying current with rapidly evolving computational methods, and balancing independent research with collaborative projects. Effective strategies include leveraging open-source statistical software, actively participating in seminars and workshops to stay updated, and establishing regular communication with interdisciplinary teams. Building a strong professional network and seeking mentorship within the department can also help in navigating research obstacles and advancing one's career.

What is the difference between Postdoctoral In Bayesian Statistics vs Postdoctoral In Data Science?

AspectPostdoctoral In Bayesian StatisticsPostdoctoral In Data Science
Required CredentialsPhD in Statistics, Mathematics, or related fieldPhD in Computer Science, Statistics, or related field
Work EnvironmentAcademic research, university labsResearch institutions, tech companies, industry labs
Employer & Industry UsageUniversities, research institutesTech firms, finance, healthcare, consulting
Common Search & Comparison IntentSpecialized research roles in Bayesian methodsBroader data analysis and machine learning roles

Postdoctoral In Bayesian Statistics focuses on advanced research in Bayesian methods within academic settings, requiring deep statistical expertise. In contrast, Postdoctoral In Data Science covers a broader range of data analysis techniques, including machine learning, often in industry environments. Both roles require a PhD but differ in application focus and work environment.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Bayesian Statistics, and why are they important?

To thrive as a Postdoctoral Researcher in Bayesian Statistics, you need an advanced degree (typically a PhD) in statistics or a related field, with strong expertise in Bayesian inference and probabilistic modeling. Proficiency with statistical programming languages such as R, Python, or Stan, and experience with specialized Bayesian analysis software are highly valued. Excellent problem-solving skills, collaboration, and the ability to communicate complex statistical concepts clearly are standout soft skills for this role. These skills and qualities are crucial for conducting rigorous research, publishing impactful results, and contributing effectively to scientific teams.
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Infographic showing various Postdoctoral In Bayesian Statistics job openings in New York as of June 2026, with employment types broken down into 4% Locum Tenens, 15% Full Time, 5% Part Time, 4% Temporary, 70% Contract, and 2% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

Quantitative Researcher (VP)

Two Sigma Investments, LP

Manhattan, NY โ€ข On-site

$165K - $325K/yr

Full-time

Medical, Dental, Retirement

Posted 3 days ago


Job description

Quantitative Researcher (VP)
Location
NY New York
United States
Business
Investment Management
Function
Quantitative Research
Experience Level
Experienced
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Position Summary
Job Location: 100 Avenue of the Americas, New York, NY 10013
Note: Company "Hybrid" work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team's requirement; telecommuting/working from home is permissible for remainder of the same month.
Duties: Conceptualize, research, analyze, and formulate innovative quantitative investment strategies across a diverse array of financial derivatives including equity futures, interest rate swaps, global bonds, credit derivatives, foreign exchange instruments, commodity futures and various types of options in global markets. Collect, preprocess and transform raw data by applying statistical and signal processing techniques including complex linear algebra, regression methods, time series methods, Bayesian statistics, neural networks, and stochastic optimizations to structure the data optimally for integration into quantitative investment models. Use advanced quantitative modeling, machine learning algorithms, optimization techniques, and rigorous statistical analysis skills to conduct research, design, and develop sophisticated quantitative/mathematics-based models that make financial investment decisions autonomously. Perform mathematical/statistical simulations to evaluate quantitative predictive models using advanced and specialized computational mathematical modeling techniques and numerical methods. Develop and implement systematic models with production-grade coding ensuring a high degree of reliability, efficiency, and scalability suitable for deployment in professional financial environments. Research and develop quantitative/computational libraries of flexible, high-reliability, highly tuned numerical code using knowledge of probability and statistical learning to increase quantitative researcher efficiency across the firm.
Minimum requirements: Master's Degree in Mathematics, Statistics, Financial Engineering, Computer Science, or related quantitative field plus 3 years of experience in Quantitative Analyst type(s) of positions.
Alternative minimum requirements: Bachelor's Degree in Mathematics, Statistics, Financial Engineering, Computer Science, or related quantitative field plus 5 years of experience in Quantitative Analyst type(s) of positions.
Skills required: Must have experience using the following quantitative skills/technologies: financial derivatives including futures and forward contracts, interest rate swaps, credit default swaps, and options; understanding of how and where various financial instruments are traded, including product-specific attributes including liquidity, seasonality, and costs to trade; data analysis skills including ability to identify, visualize, and validate statistical patterns in large financial and nonfinancial datasets; metrics used to evaluate systematic investment strategies including Sharpe ratios, drawdowns, and correlations; statistics including complex linear algebra and linear models, probability theory, pattern recognition, regression methods, time series methods, neural networks, Bayesian statistics, and their applications in real-world data analysis; optimization theory and algorithms including linear and non-linear optimization, convex optimization, stochastic optimization, and their applications in finance; Python and SQL with ability to write highly reliable and efficient computer programs; Linux/Unix operating systems; version control collaboration software (Git, SVN); and machine learning algorithms including elastic nets, neural networks, and tree-based models.
Base salary: The base pay for this role will be between $165,000 and $325,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental, and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications, and experience.
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