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Causal Inference Machine Learning Postdoctoral Jobs in Saint John, IN

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... and inference serving frameworks such as Triton Experience with hosting computer vision model ...

Tavern Research

Data Scientist I

Chicago, IL · On-site

$95K - $113K/yr

Learn and apply best practices and emerging tools in machine learning, AI, and causal inference. Qualifications We know it's rare to check every box. If you meet most of these, we encourage you to ...

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All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Saint John, IN

Causal Inference Machine Learning Postdoctoral information

See Saint John, IN salary details

$32.3K

$49.3K

$55.5K

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

As of Jul 26, 2026, the average yearly pay for causal inference machine learning postdoctoral in Saint John, IN is $49,337.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,700.00 and $51,400.00 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 cities near Saint John, IN are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Saint John, IN with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Senior/Staff Data Scientist, Consumer Apps - Klover

Senior/Staff Data Scientist, Consumer Apps - Klover

Attain

Chicago, IL • On-site, Remote

Other

Posted 23 days ago

Job description

About Attain

Built for consumers and companies, alike. 

Klover's engineering team powers one of the fastest-growing fintech platforms in the U.S., supporting over one million active users each month. Our systems process and move more than $1.5 billion annually, enabling real-time access to financial tools, rewards, and services that help people improve their day-to-day lives.

As part of this team, you'll help design, build, and scale the systems that underpin Klover's core products and platform. You'll work on high-impact, production-grade systems that prioritize reliability, security, and performance, and that integrate with a broad ecosystem of internal and external services. The work you do will directly shape how users interact with Klover's products, access their money, and experience transparent, low-fee financial services.

Klover engineers collaborate closely with colleagues across backend, frontend, data science, and product teams to deliver scalable, high-quality solutions for a rapidly growing user base. You'll have the opportunity to work with modern technologies and architectures while helping define and evolve the next generation of inclusive, data-powered financial products-building systems and interfaces that emphasize reliability, privacy, and performance at scale.

About the role

Attain is seeking a Senior/Staff Data Scientist to support the growing needs of our suite of B2C financial services. This role will be highly hands-on, focused on building, improving, validating, and deploying predictive models that power consumer decisioning and business optimization across our app portfolio.

You will work on advanced machine learning and statistical modeling problems, including cash-flow based credit decisioning for our earned wage advance product, Klover, as well as consumer behavior modeling, transaction categorization, paycheck detection, fraud scoring, churn prediction, and other high-impact predictive modeling use cases. The ideal candidate combines strong quantitative fundamentals with practical experience building models and analytical systems from scratch.

Attain Office Hybrid Schedule: 

  • Chicago, IL: 4 days in-office; 1 day remote

What a typical week might look like

  • Hands-on development of ML and statistical models at the core of our EWA product, with a focus on fast, rigorous, and high-quality execution
  • Build and improve predictive models across consumer decisioning, consumer behavior modeling, fraud, churn, transaction intelligence, and other business-critical use cases
  • Own the full model development lifecycle, including data exploration, feature engineering, model training, validation, deployment, monitoring, and retraining
  • Develop reusable modeling pipelines, analytical tools, and production-quality code to support scalable data science work
  • Apply strong statistical and mathematical judgment to model evaluation, calibration, robustness testing, and business impact measurement
  • Collaborate with data analysts, engineers, product managers, and business stakeholders to deliver ML models with quality, efficiency, and precision
  • Identify new areas where data science, predictive modeling, and optimization can improve product and business outcomes

Preferred Qualifications 

  • 5+ years of direct experience working as a Data Scientist, Machine Learning Scientist, Model Developer, Applied Scientist, Economist, or similar role on relevant business problems
  • Strongly preferred: Master's, or Ph.D. in a STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field
  • Demonstrated ability to apply critical thinking, causal inference, abstract reasoning, and generalization to complex, ambiguous business and technical problems
  • Strong expertise developing, validating, deploying, and monitoring machine learning models in production
  • Experience with AI/ML-assisted development tools and MLOps practices, including experience working with large language models (LLMs) or autonomous agents for code generation and model refinement
  • Experience with predictive modeling, consumer behavior modeling, risk modeling, credit decisioning, fraud modeling, churn modeling, or other high-impact applied ML use cases
  • Solid foundation in statistics, probability, mathematics, and machine learning fundamentals
  • Strong Python coding skills, with the ability to build models, pipelines, and analytical tools from scratch
  • Strong SQL skills and experience working with large, messy, real-world datasets
  • Experience with feature engineering, model evaluation, calibration, monitoring, retraining, and model performance diagnostics
  • Experience with cloud computing services or platforms; GCP preferred
  • Familiarity with version control, peer code review, and collaborative software development practices
  • Demonstrated ability to learn new technologies, applications, and modeling approaches quickly
  • Willingness to roll up your sleeves and wear multiple hats across data science, analytics, modeling, and technical execution based on business needs
  • Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences

We're excited to hear from you. 

At Attain, we are passionate about finding people to continuously help us grow our organization. We encourage you to apply, even if your experience doesn't match every detail of the job description. If we don't see something that immediately fits, we will keep your resume on file for future opportunities.

Apply