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Causal Inference Machine Learning Postdoctoral Jobs in Indianapolis, IN

Elevance Health

Gen AI Engineer

Indianapolis, IN · Hybrid

Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ... LLM caching strategies for inference optimization; RAG architecture design and implementation.

Elevance Health

Gen AI Engineer

Indianapolis, IN · Hybrid

Collaborate closely with the MLOps, product teams, business stakeholders, machine learning ... LLM caching strategies for inference optimization; RAG architecture design and implementation.

Cisco

Applied AI Scientist

Indianapolis, IN

Develop and enhance distributed training and inference workflows, leveraging data-driven approaches ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

Allied Solutions LLC

Senior AI Architect

Carmel, IN · On-site

... and inference services. • Lead architectural planning for advanced use cases such as NLP ... Azure Machine Learning, AWS SageMaker, Google Vertex AI, Databricks, and OpenAI APIs. • ...

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Causal Inference Machine Learning Postdoctoral information

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$33.9K

$51.8K

$58.3K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in Indianapolis, IN is $51,830.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,100.00 and $54,000.00 per year, depending on experience, location, and employer.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Indianapolis, IN look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Indianapolis, IN are:
What cities near Indianapolis, IN are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Indianapolis, IN with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Real World Biostatistician - RWE CMH experience

Real World Biostatistician - RWE CMH experience

Syneos Health/ inVentiv Health Commercial LLC

Indianapolis, IN • On-site

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Syneos Health rating

8.1

Company rating: 8.1 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

32nd of 74 rated pharmaceutical

Job description

Real World Biostatistician - RWE CMH experience

Syneos Health is a leading fully-integrated life sciences services organization built to accelerate customer success. We partner with innovators at every point across the drug development and commercialization continuum, helping them navigate complexity, anticipate change and accelerate progress.
Our Clinical Solutions team members act with a drug development mindset, applying their years of experience and deep expertise to truly understand customer needs and represent those in the solutions we shape.
Whether you join us in a Functional Service Provider partnership or a Full-Service environment, you'll collaborate with passionate problem solvers, innovating as a team to help our customers achieve their goals. We are agile and driven to deliver - for one another, our customers, and, most importantly, for those in need.
Discover what your 25,000 future colleagues already know:
Why Syneos Health
* We are passionate about developing our people, through career development and progression; supportive and engaged line management; technical and therapeutic area training; peer recognition and total rewards program.
* We are committed to building an inclusive culture - where you can authentically be yourself. Central to this is our purpose - Driven to Deliver - which captures the passion of our colleagues to show up each day and shape solutions that have the ability to dramatically impact someone's life.
* We are continuously building the company we all want to work for and our customers want to work with. Why? Because we know that when we bring together smart colleagues from across the world, we can shape the future of healthcare, driving impact for customers and defining the pace of patient progress.

Job Responsibilities

Job Description

Biostatistician - Real-World Evidence (RWE)

Role Overview

We are seeking a biostatistician with strong experience in real-world data (RWD) with observational study design and safety study experience in addition to RWE CMH experience. This role will support evidence generation across multiple therapeutic areas. This role will focus on the design, analysis, and interpretation of observational studies using EMR and claims data toinform:clinical development, HEOR, regulatory strategy, and market access.

Key Responsibilities

  • Design and execute real-world evidence (RWE) studies using EMR and claimsdata;Conducting data specs,SAPand protocol with key research objectives

  • Develop and apply robust statistical methodologies, including:

  • Causal inference methods (e.g., propensity score methods, weighting, matching; GLM or GLMM, MMRM; survival analysis; random forest)

  • Trial emulation frameworks

  • External control arm development and borrowing strategies

  • Perform data analysis using healthcare coding systems (e.g., ICD, NDC)

  • Conduct sample size estimation and power calculations for observational and hybrid study designs

  • Collaborate cross-functionally with stakeholders across:

  • HEOR

  • Market Access

  • Regulatory

  • Clinical Development

  • Translate complex analytical results into clear, actionable insights,e.g.powerpointor study report for decision-making

  • Support methodological innovation in RWE, including integration of machine learning approaches where appropriate

Required Qualifications

  • M.S. or Ph.D. in Biostatistics, Statistics, Epidemiology, or related field

  • 5 years of experience in RWD/RWE analytics (industry or equivalent)

  • Strong experience with EMR and/or claims data

  • Proficiencyin healthcare coding systems (e.g., ICD, NDC)

  • Programmingexpertisein at least one of: SAS, R, or Python

  • Working knowledge with SQL logic and OMOP data structures

  • Solid understanding of:

  • Causal inference methods

  • Observational study design

  • Sample size and power considerations

  • Some examples: Independently write cohort definitions in SQL logic; Debug data issues e.g., time zero alignment, exposure gaps; Understand concept mapping (ICD SNOMED RxNorm); Translate statisticalestimand censoring rule and data extraction logic

  • RWE CMH Experience

Preferred Qualifications

  • Ph.D. strongly preferred

  • Experience in one or more therapeutic areas:

  • Diabetes

  • Cardiovascular disease

  • Metabolic disorders

  • Familiarity with:

  • Trial emulation methodologies

  • External control borrowing / hybrid designs

  • Basic machine learning methods applied to RWD

  • Demonstrated ability to work across multiple therapeutic areas (TAs) in a fast-paced environment

  • Strong communicationand stakeholder engagement skills

  • Advanced (nice-to-have, not alwaysrequired)

  • Build reusable cohort pipelines

  • Optimizequeries for large-scale databases

  • Work across multiple CDMs (OMOP, Sentinel,PCORnet)

Core Competencies

  • Analytical rigor and methodological depth

  • Cross-functional collaboration

  • Ability tooperatewith agility across diverse projects and therapeutic areas

  • Clear and effective scientific communication

Get to know Syneos Health


Over the past 5 years, we have worked with 94% of all Novel FDA Approved Drugs, 95% of EMA Authorized Products and over 200 Studies across 73,000 Sites and 675,000+ Trial patients.

No matter what your role is, you'll take the initiative and challenge the status quo with us in a highly competitive and ever-changing environment. Learn more about Syneos Health.

http://www.syneoshealth.com

Additional Information


Tasks, duties, and responsibilities as listed in this job description are not exhaustive. The Company, at its sole discretion and with no prior notice, may assign other tasks, duties, and job responsibilities. Equivalent experience, skills, and/or education will also be considered so qualifications of incumbents may differ from those listed in the Job Description. The Company, at its sole discretion, will determine what constitutes as equivalent to the qualifications described above. Further, nothing contained herein should be construed to create an employment contract. Occasionally, required skills/experiences for jobs are expressed in brief terms. Any language contained herein is intended to fully comply with all obligations imposed by the legislation of each country in which it operates, including the implementation of the EU Equality Directive, in relation to the recruitment and employment of its employees. The Company is committed to compliance with the Americans with Disabilities Act, including the provision of reasonable accommodations, when appropriate, to assist employees or applicants to perform the essential functions of the job.

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