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Postdoctoral In Bayesian Statistics Jobs in Indiana

TAO Digital Solutions

Senior ML Engineer

Indianapolis, IN

$99K - $137K/yr

Strong foundation in statistical inference, econometrics, and causal analysis (e.g., regression, Bayesian methods, DiD). * Proficiency in Python, SQL, and ML frameworks (Scikit-Learn, XGBoost ...

Diverse Lynx

Databricks Data Scientist

Indianapolis, IN · On-site

... Bayesian methods for epidemiology and health economics (HEOR) studies • Ensure all models and ... Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or a ...

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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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Postdoctoral In Bayesian Statistics jobs near you

Senior ML Engineer

Tao Digital Solutions, Inc.

Indianapolis, IN • On-site

$99K - $137K/yr

Full-time

Posted 17 days ago

Job description

We are building a small, high-impact team to support advanced analytics and machine learning initiatives for a leading healthcare technology management company. This team will focus on predictive modeling, cost optimization, and ROI analysis for clinical asset management and capital planning.
Key Responsibilities
  • Develop and deploy statistical and machine learning models for predictive maintenance, resource optimization, and operational efficiency.
  • Perform econometric and financial analysis to support capital planning and cost-benefit decisions.
  • Design and implement data pipelines for large-scale healthcare datasets (EHR, claims, RTLS, device telemetry).
  • Collaborate with cross-functional teams (clinical engineering, finance, IT) to translate insights into actionable strategies.
  • Ensure compliance with HIPAA and healthcare data governance standards.

Required Qualifications
  • Master's or Ph.D. in Statistics, Economics, Data Science, or related field.
  • 3+ years of experience in ML model development and deployment.
  • Strong foundation in statistical inference, econometrics, and causal analysis (e.g., regression, Bayesian methods, DiD).
  • Proficiency in Python, SQL, and ML frameworks (Scikit-Learn, XGBoost, TensorFlow).
  • Excellent communication skills for presenting insights to technical and business stakeholders.

Preferred Skills
  • Experience with healthcare data (EHR, claims, RTLS).
  • Familiarity with capital planning and ROI modeling.
  • Knowledge of cloud platforms (AWS, Azure) and containerized deployments.

Apply