ZipRecruiter

Log In

1

Causal Inference Machine Learning Postdoctoral Jobs in Delaware

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

Block

... causal reasoning, agentic systems, and product intelligence. The goal is not simply to build ... Preference and goal inference * Learning when intervention creates value versus friction Agentic ...

next page

Showing results 1-20

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in Delaware

Causal Inference Machine Learning Postdoctoral information

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 Delaware?

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

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Delaware look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Delaware are:

What cities in Delaware are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities in Delaware with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Delaware as of June 2026, with employment types broken down into 4% As Needed, 66% Full Time, 17% Part Time, 4% Temporary, and 9% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Vice President, Digital Marketing Analytics

Fairygodboss

Wilmington, DE • On-site

$180 - $280/hr

Other

Medical, Retirement

Posted 12 days ago

Job description

You will help shape digital marketing strategy through rigorous analysis, experimentation, and clear storytelling that drives business outcomes. You will work with partners across Marketing, Finance, Product, and Technology to turn complex questions into measurable actions. You will grow and lead a high-performing analytics team, building scalable, reliable ways of working that improve how marketing decisions are made.

As a Vice President, Digital Marketing Analytics at JPMorganChase within the Digital Marketing Analytics team, you will lead quantitative research and experimentation to optimize marketing performance and customer experiences. You will translate customer behavior and campaign performance into insights that leaders can act on, and you will build team capability through coaching, hiring, and a culture of scientific rigor and responsible use of advanced analytics. You will take a pragmatic approach to generative AI and large language models (LLMs) as complementary tools, while relying on classical statistical and causal methods for core measurement and optimization workstreams.

Job responsibilities
  • Deliver effective quantitative problem solving and analytical research to support key digital marketing initiatives
  • Apply deep business understanding and advanced analytical techniques to drive research, experimentation, and measurable outcomes
  • Lead, mentor, hire, and develop a high-performing analytics team; promote scientific rigor, ethical AI, and continuous learning
  • Maintain a pragmatic approach to generative AI and large language models (LLMs) as complementary tools, prioritizing classical statistical methods for core measurement and optimization work
  • Partner with cross-functional teams (for example, Marketing and Finance) to drive insights into action and deliver business impact
  • Design and implement scalable, reliable analytics processes to optimize business outcomes
  • Conduct extensive analysis of marketing performance, measurement configuration and settings, and customer behavior to improve channel strategies and optimization using advanced quantitative methods
  • Own and support strategic measurement initiatives, including effectiveness evaluation and profit and loss analysis; quantify statistical and practical significance
  • Solve unstructured business problems and develop deep-dive analyses of customer behavior using multiple analytics and statistical techniques
  • Conduct hypothesis testing and advanced experimental design (A/B and multivariate tests) to measure the impact and effectiveness of marketing strategies
Required qualifications, capabilities and skills
  • Graduate or post-graduate degree in a quantitative discipline (for example, Computer Science, Statistics, Mathematics, Finance, Economics, Data Analytics, or Machine Learning)
  • 5+ years of hands‑on analytics experience in banking strategic analytics
  • Hands‑on proficiency with Python and SQL; experience with visualization tools (for example, Tableau) and analytics tools (for example, Alteryx)
  • Experience with Adobe Analytics (implementation, reporting, and insight generation)
  • Strong statistical or econometric foundation with hands‑on experimentation and measurement experience, including A/B testing, causal inference, and experimentation frameworks
  • Strong advanced analytics skills using SAS, Python, or R
  • Excellent communication skills with the ability to translate complex models into clear explanations and reason codes, influencing cross‑functional stakeholders and senior leadership
  • Exposure to enterprise AI enablement, LLM‑assisted workflows, or analytics transformation programs
  • Ability to evaluate opportunities to apply AI, generative AI, and intelligent automation to improve investigative analysis, documentation, operating procedures, knowledge retrieval, issue summarization, and workflow efficiency
  • Familiarity with supervised learning, anomaly detection, semi‑supervised learning, clustering, feature stores, calibration and threshold optimization, and imbalanced learning
Preferred qualifications, capabilities and skills
  • Proficiency in big data extract, transform, and load processes across structured and unstructured data sources
  • Professional experience with AWS, Spark or EMR, and Snowflake
  • Experience with Confluence and generative AI tools (for example, ChatGPT), subject to firm‑approved usage
  • People leadership experience, including recruiting, coaching, performance management, and fostering an inclusive, high‑accountability culture
  • Strong understanding of IT processes and databases, with the ability to work directly with data owners and custodians
ABOUT US

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission‑based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on‑site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

ABOUT THE TEAM

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most‑used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

#J-18808-Ljbffr

Apply