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Causal Inference Phd Internship Jobs in Delaware

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Causal Inference Phd Internship information

What is a causal inference PhD internship?

A Causal Inference PhD Internship is a specialized research position for doctoral students focused on causal inference, which involves determining cause-and-effect relationships from data. Interns typically work with large datasets, advanced statistical models, and machine learning techniques to answer questions about how variables influence one another. These internships are often offered by tech companies, research labs, or policy organizations and provide hands-on experience in designing experiments, analyzing observational data, and developing new methodologies. The goal is to bridge academic research with real-world applications, contributing to projects that require rigorous causal analysis.

What types of projects does a causal inference PhD intern typically work on during their internship?

Causal Inference PhD interns often engage in projects that involve designing and analyzing experiments or observational studies to draw valid conclusions about cause-and-effect relationships. These projects might include developing statistical models, collaborating with data scientists and product teams, and presenting findings to inform business or policy decisions. Interns usually have the opportunity to work with large-scale, real-world data, and are encouraged to publish or present their work at conferences, supporting both professional growth and academic development.

What are the key skills and qualifications needed to thrive as a causal inference PhD intern, and why are they important?

To thrive as a Causal Inference PhD Intern, you need a strong background in statistics, econometrics, and causal inference methods, often supported by advanced graduate studies in a related field. Familiarity with statistical programming languages such as R or Python, and experience using data analysis tools and frameworks like Stata or TensorFlow Probability, are typically required. Excellent problem-solving abilities, critical thinking, and the ability to communicate complex concepts clearly help you stand out in this role. These skills and qualities are crucial for designing robust experiments, drawing reliable conclusions, and effectively collaborating with interdisciplinary research teams.

What is the difference between Causal Inference Phd Internship vs Data Scientist Internship?

AspectCausal Inference Phd InternshipData Scientist Internship
Required CredentialsPhD in statistics, economics, or related fieldBachelor's or Master's in CS, statistics, or related field
Work EnvironmentResearch-focused, academic or industry research teamsData analysis, modeling, and business insights
Employer & Industry UsageResearch institutions, tech companies, financeTech firms, startups, finance, healthcare
Search & Comparison IntentFocus on causal inference research rolesBroader data analysis roles

While a Causal Inference Phd Internship emphasizes research in causal analysis with advanced credentials, a Data Scientist Internship covers broader data analysis skills suitable for various industries. Both roles involve working with data, but their focus, required background, and career paths differ significantly.

What are popular job titles related to Causal Inference Phd Internship jobs in Delaware?

For Causal Inference Phd Internship jobs in Delaware, the most frequently searched job titles are:

What job categories do people searching Causal Inference Phd Internship jobs in Delaware look for?

The top searched job categories for Causal Inference Phd Internship jobs in Delaware are:

What cities in Delaware are hiring for Causal Inference Phd Internship jobs?

Cities in Delaware with the most Causal Inference Phd Internship job openings:

Causal Inference Phd Internship jobs near you
Infographic showing various Causal Inference Phd Internship job openings in Delaware as of June 2026, with employment types broken down into 47% Full Time, 48% Part Time, and 5% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.

Data Scientist (Card Data & Analytics Strategy)-Executive Director

JPMorgan Chase & Co.

Wilmington, DE • On-site

$180 - $260/hr

Other

Posted 3 days ago

New

JPMorgan Chase & Co. rating

8.0

Company rating: 8.0 out of 10

Based on 495 frontline employees who took The Breakroom Quiz

72nd of 171 rated banks

Job description

Join JPMorganChase's Card business as a Data Scientist Director leading the Customer & Strategic Analytics team within Card Data & Analytics. This is a senior leadership role that requires technical depth in AI/ML, the ability to translate between business strategy and technical execution, and a track record of building high-performing analytics teams.

You’ll lead a group of analytics leaders, data scientists, and analysts responsible for delivering AI and analytics solutions that shape product strategy, customer experience, and competitive positioning across the Card portfolio. The role spans three core areas: setting AI and analytical direction across multiple business domains, serving as the bridge between senior business stakeholders and technical teams, and building organizational capability through talent development, inclusive culture, and operational excellence.

Job Responsibilities
  • Define and drive the AI and analytics strategy for Card D&A, identifying high-value opportunities for generative AI, agentic AI, and advanced analytics to create competitive advantage
  • Stay current on emerging AI/ML techniques and evaluate new capabilities, tools, and vendor offerings for practical application in the Card business
  • Partner with Data, Product, Technology, Risk, and Finance to deliver AI and ML solutions from ideation through production deployment, ensuring solutions are scalable, responsible, and aligned to business needs
  • Lead analytics supporting customer experience, benefits, product design, portfolio performance, and pricing and targeting strategies
  • Drive measurement frameworks, experimentation (including A/B testing and causal inference), and personalization strategies that improve customer experience and benefits utilization
  • Build and lead competitive intelligence capabilities that monitor market trends, competitor positioning, and industry benchmarks, partnering with external vendors and synthesizing internal and external data to give senior leaders a clear view of the competitive landscape
  • Deliver forward-looking analyses that inform strategic planning and product roadmap decisions
  • Serve as the connective tissue between business strategy and technical execution translating business problems into analytical frameworks and translating model outputs into executive-ready recommendations
  • Define analytical priorities with senior stakeholders, interpret results, and drive data-informed decisions across product, marketing, and servicing strategies
  • Lead, mentor, and develop a multi-layered team of analytics leaders, data scientists, and analysts, setting clear goals and performance expectations and providing ongoing coaching across all levels
  • Attract and retain top analytics talent through hiring, onboarding, and skills development programs

  • Champion a culture of innovation, intellectual rigor, inclusion, and collaborative problem-solving across the broader Card D&A organization

Required qualifications, capabilities, and skills
  • Master's or PhD in a quantitative field and 10+ years of progressive analytics experience
  • Senior leadership experience managing and developing multi-disciplinary analytics teams, including managers and individual contributors with strong coaching, org design, and talent development skills
  • Strong technical foundation in AI/ML, including experience evaluating and adopting emerging techniques, guiding architecture decisions, and moving solutions from prototype to production
  • Working knowledge of GenAI and agentic AI patterns, including large language models, retrieval-augmented generation, and agentic frameworks, with the ability to assess where they add value vs. simpler approaches
  • Exceptional ability to translate between technical and business audiences, with a track record of influencing senior leaders and cross-functional partners
  • Experience scoping and prioritizing a portfolio of analytical workstreams across multiple business domains in a large enterprise environment
  • Proficiency in Python and/or R, and experience with modern data platforms such as Snowflake or Databricks
  • Experience with causal inference, A/B testing, and experimentation frameworks at scale
Preferred qualifications, capabilities, and skills
  • Experience in Card, consumer lending, or payments analytics, including familiarity with installment products, credit risk, or customer lifecycle management
  • Experience building or leading competitive intelligence functions using alternative data, market data, or external benchmarking
  • Familiarity with data architecture concepts (ingestion, modeling, governance, quality) and MLOps/observability practices
  • Familiarity with responsible AI principles, model governance, and regulatory considerations in financial services
  • Experience enabling analytics adoption through change management, self-service tooling, or organizational enablement
  • Familiarity with Agile delivery methods and modern product practices
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