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Manager Causal Inference Jobs in Tennessee (NOW HIRING)

Manager Causal Inference information

What are the key skills and qualifications needed to thrive as a Manager of Causal Inference, and why are they important?

To thrive as a Manager of Causal Inference, you need a deep understanding of statistics, econometrics, and experimental design, typically supported by an advanced degree in a quantitative field. Proficiency with data analysis tools such as R, Python, SQL, and specialized causal inference libraries, along with experience using data visualization and project management platforms, is crucial. Strong leadership, communication, and critical thinking skills help you effectively guide teams and translate complex findings to stakeholders. These skills ensure rigorous, actionable insights that drive strategic decision-making and organizational impact.

How does a Manager of Causal Inference typically collaborate with cross-functional teams to drive impactful business insights?

Managers of Causal Inference frequently work alongside data scientists, product managers, engineers, and business leaders to design and execute experiments that reveal the true impact of business decisions. They translate complex statistical findings into actionable recommendations, ensuring stakeholders understand both the methodology and implications. Regularly, they lead discussions on experiment design, data collection strategies, and result interpretation, fostering a culture of evidence-based decision-making across the organization.

What does a Manager Causal Inference do?

A Manager Causal Inference leads teams that analyze data to determine cause-and-effect relationships, often in business, healthcare, or technology settings. They design experiments or use statistical methods to understand how different factors influence outcomes, helping organizations make data-driven decisions. This role typically involves managing projects, overseeing analysts or data scientists, and communicating findings to stakeholders. Strong expertise in statistics, data analysis, and leadership is essential for success in this position.
What are the most commonly searched types of Causal Inference jobs in Tennessee? The most popular types of Causal Inference jobs in Tennessee are:
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What cities in Tennessee are hiring for Manager Causal Inference jobs? Cities in Tennessee with the most Manager Causal Inference job openings:
Postdoctoral Scholar-Pediatrics Research

Postdoctoral Scholar-Pediatrics Research

The University of Tennessee

Memphis, TN • On-site

Full-time

Posted 14 days ago


Job description

The University of Tennessee Health Science Center Department of Pediatrics Research seeks a highly motivated and innovative Postdoctoral Scholar. This position offers a unique opportunity to contribute to cutting-edge research at the intersection of artificial intelligence, machine learning, and healthcare. The successful candidate will develop and apply advanced machine learning techniques (e.g. explainable, ethical, empathic and agentic AI), natural language processing (NLP), large language models (LLMs), data science methods, and mHealth to analyze large-scale, multidimensional, and multimodal datasets. The goal is to generate predictive models that provide valuable causal and probabilistic insights into clinical and population health outcomes.

In addition to technical research responsibilities, the incumbent will play a key role in implementing and managing a multi-phase study designed to improve HPV vaccination uptake among adolescents.

Ph.D. in a relevant discipline (e.g. Medical Informatics, Computer Science, Data Science, Computational Social Science, Epidemiology, Biostatistics, etc.)

Strong research track record, programming, and implementation skills, along with excellent communication, organizational, and project management skills. Experience in both qualitative and quantitative research and studying and implementing public health interventions are highly desirable.

  1. Develops and applies advanced ML algorithms, NLP models, and LLMs to analyze complex data. 
  2. Designs and implements novel data science methodologies for predictive modeling, causal inference, and probabilistic analysis in clinical and population health contexts. 
  3. Conducts data mining and knowledge discovery from multidimensional and multimodal datasets. 
  4. Collaborates with a diverse team of researchers, clinicians, and public health experts to translate data insights into actionable outcomes. 
  5. Contributes to scientific publications, presentations, and grant applications.
  6. Performs other duties as assigned.