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Causal Inference Machine Learning Postdoctoral Jobs in Minnesota

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Data Scientist, Marketing

Richfield, MN · Hybrid

Develop statistical and machine learning models to improve marketing effectiveness - e.g., marketing mix models, marketing attribution models, causal inference models, uplift models, etc. * Develop ...

Deloitte

AI and Data Science Engineer III

Minneapolis, MN · On-site +1

$119.50K - $143.50K/yr

Deliver governed datasets and feature engineering and serving patterns for machine learning training and real-time inference, including online and offline consistency, caching, latency targets, and ...

Mayo Clinic

Research Fellow in Surgical AI

Rochester, MN

$67.28K - $78.71K/yr

... postdoctoral fellow to join our Surgical Outcomes and Oncology research team in The Department of Surgery at Mayo Clinic in the field of Machine Learning and Artificial Intelligence (AI). The ...

Deloitte

AI Data Engineer Senior Consultant

Minneapolis, MN · On-site

$119.50K - $143.50K/yr

... machine learning training and real-time inference, including online and offline consistency, caching, latency targets, and backfills • Implement privacy, access, quality, lineage, monitoring ...

Mayo Clinic

Research Fellow - Engineering

Rochester, MN · On-site

$64.79K - $80.99K/yr

... postdoctoral Research Fellow for the CT Clinical Innovation Center, a worldwide leader in the field ... Skills in the development of machine learning algorithms for medical imaging tasks are desirable.

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Causal Inference Machine Learning Postdoctoral information

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 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 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 are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Minnesota? For Causal Inference Machine Learning Postdoctoral jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Minnesota look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Minnesota are:
What cities in Minnesota are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Minnesota with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Machine Learning Engineer- AI Data Platform (Minneapolis, MN)

Machine Learning Engineer- AI Data Platform (Minneapolis, MN)

MOBE, LLC

Minneapolis, MN

$119.50K - $143.50K/yr

Other

Posted 25 days ago

Job description

Company Overview MOBE helps people discover new ways to live healthier. We are the whole-person, cross-condition solution that goes further to deliver better health and lower overall costs through evidence-based individual health guidance and pharmacist-led medication management. We empower individuals to make meaningful changes that improve their health and overall well-being.

Behind our innovative solutions are robust data analytics, digital application, and a uniquely human philosophy. With one-to-one connection and compassion, we uncover opportunities, overcome challenges, and motivate people to transform their lives. At MOBE our team is our most significant asset.

We cultivate a culture grounded in curiosity, innovation, and growth. We encourage new ideas, fresh solutions, and meaningful impact. We value a workforce made up of people with differences who are eager to learn from each other and grow personally and professionally.

We extend this approach to our partners and communities, seeking to increase understanding and expand opportunities across all groups. Your role at MOBE We are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI-driven insights across the organization.You will build intelligent data pipelines, production-grade ML systems, and AI-enabled features that translate complex data into actionable outcomes

This role is ideal for an engineer who enjoys working end-to-end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools. **Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time

Responsibilities: Build AI-first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform. Deploy production ML systems : Develop, deploy, and monitor machine learning models using AWS SageMaker, ensuring reliability, observability, and performance in production environments. Implement Retrieval-Augmented Generation (RAG): Architect and maintain RAG-based systems that combine structured and unstructured data to power AI-driven insights and applications.

Operationalize ML lifecycle management : Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement. Design feature infrastructure : Build and manage feature stores (e.g., Feast, Tecton, or SageMaker Feature Store) to ensure consistent, reusable features across training and inference. Orchestrate complex workflows : Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring

Enable analytics consumption : Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub. Translate business questions into AI solutions : Collaborate with stakeholders to convert ambiguous business problems into measurable ML- and data-driven solutions. Uphold data quality and governance : Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.

Collaborate cross-functionally : Work closely with Data Science, Analytics Engineering, Medical Economics, and DataOps to align AI platform capabilities with business priorities.

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