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Causal Inference Machine Learning Postdoctoral Jobs in Indianapolis, IN

Pwc

AI Engineer

Indianapolis, IN · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Info Way Solutions

Gen AI Engineer

Indianapolis, IN · On-site

... optimize inference pipelines for real-time or batch generation. • Collaborate with cross ... Required : • Bachelor's or Master's in Computer Science, Machine Learning, or related field. • ...

Cisco

Applied AI Scientist

Indianapolis, IN · On-site

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 ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Indianapolis, IN

Causal Inference Machine Learning Postdoctoral information

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

As of Sep 3, 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?

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.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Indianapolis, IN?

For Causal Inference Machine Learning Postdoctoral jobs in Indianapolis, IN, the most frequently searched job titles are:

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

Associate Director - AI/ML Data Scientist

Scorpion Therapeutics

Indianapolis, IN • On-site

$120 - $190/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 4 days ago

Job description

Position Overview:

Senior Data Scientist to develop advanced analytics, machine learning, and AI that convert governed workforce data into actionable intelligence for Lilly's People Intelligence platform.

What You'll Be Doing:
  • - Design, validate, and operationalize statistical/ML/AI models for workforce use cases.
  • - Build predictive capabilities for attrition, talent risk, employee experience, hiring, mobility, skills, org health, and workforce planning.
  • - Use regression/classification/clustering/forecasting, causal inference, NLP, anomaly detection, and scenario modeling.
  • - Develop AI-enabled analytics (risk signals, probabilistic guidance, recommended follow-ups); create reusable AI skills/workflows/prompts.
  • - Build semantic models and make models AI-ready for Fabric; integrate into Fabric Data Agents, Power BI, MCP, and approved enterprise experiences.
  • - Establish governance/measurement: validation, monitoring, explainability, retraining/retirement; define performance thresholds; manage bias/fairness/privacy/unintended consequences; ensure reproducible methods.
  • - Advise HR leaders and product owners; translate findings into decision-oriented language; convert analyses into reusable models/metrics/products; coach others.
Key Deliverables:

Predictive/causal workforce models; governed semantic data models; generative AI text analytics (sentiment/theme extraction); model accuracy/testing frameworks; monitoring/governance standards; reusable ML pipelines/feature stores; scenario planning/workforce simulations; explainable AI outputs.

Basic Qualifications:
  • - BS in data science/statistics/ML or related.
  • - 4+ years Python proficiency; develop/validate/operationalize predictive models.
  • - 5+ years statistical inference, experimental design, model evaluation, data-quality assessment.
Strongly Preferred:
  • - Advanced degree (quantitative/behavioral) and/or AI certification.
  • - Large complex longitudinal datasets; executive communication.
  • - Workforce/people analytics; independent judgment with sensitive employee data.
  • - Microsoft Fabric/Spark/Power BI/Azure AI/Fabric Data Agents.
  • - NLP, generative AI/RAG, causal inference, organizational network analysis, workforce forecasting; responsible AI/privacy/governance familiarity.
Additional Information (Benefits):
  • - Eligible for company bonus (full-time equivalent).
  • - Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits (e.g., FSA), life insurance, time off/leave of absence, well-being benefits (e.g., EAP, fitness, employee clubs/activities).
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