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Environmental Data Science Jobs in Delaware (NOW HIRING)

Winaxis LLC

Data Analyst

Lewes, DE · On-site +1

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or related field ... Ability to work independently and collaborate effectively in a team environment. * Excellent ...

Td

AML Business Data Manager

Wilmington, DE

$96K - $155K/yr

... environment. A working knowledge of trade lifecycle events and their intersection with AML and ... Helps the organization understand the principles and the math behind the scientist process to drive ...

Implify Inc

Data Scientist PCON & Architect

Newark, DE · On-site

... environment to deliver analytics solution to client Define new business metrics as required and ... Science, Engineering or Physics 7+ years applied experience devising, deploying and servicing ...

Sallie Mae

Senior Manager, Statistical Modeling

Newark, DE · On-site

$85K - $104K/yr

... Data Science, Machine Learning, or a related field. * 5+ years of experience in statistical modeling, including hands-on experience developing and deploying models in production environments.

AAA Mid Atlantic

Marketing Data Specialist

Wilmington, DE · On-site

$65K - $94K/yr

Minimum Qualifications: * Bachelor's Degree in Computational and Data Science, Economics ... Ability to work independently in fast paced environment with competing priorities, excellent oral ...

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All JobsEnvironmental Data Science JobsEnvironmental Data Science Jobs in Delaware

Environmental Data Science information

See Delaware salary details

$37.5K

$122.8K

$196.7K

How much do environmental data science jobs pay per year?

As of Jul 1, 2026, the average yearly pay for environmental data science in Delaware is $122,844.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,600.00 and $136,100.00 per year, depending on experience, location, and employer.

What does an environmental data scientist do?

An environmental data scientist analyzes environmental data to identify patterns, assess environmental risks, and support decision-making. They use statistical tools, programming languages like Python or R, and GIS software to interpret data related to climate, pollution, and natural resources, often working in research or consulting settings.

Can data scientists make $300k?

Environmental data scientists can potentially earn $300,000 or more at senior levels or in specialized roles, especially with extensive experience, advanced skills in machine learning, and working in high-demand industries or organizations. However, such salaries are typically achieved through seniority, leadership positions, or in regions with higher compensation standards.

What is Environmental Data Science?

Environmental Data Science is an interdisciplinary field that uses statistical, computational, and analytical techniques to collect, analyze, and interpret large sets of data related to the environment. Professionals in this field work on issues like climate change, pollution, biodiversity, and natural resource management by extracting meaningful insights from complex environmental datasets. Their work supports decision-making for policy, conservation, and sustainability initiatives. Environmental data scientists often collaborate with ecologists, geographers, and policymakers to address environmental challenges using data-driven approaches.

Is 40 too late for data science?

Environmental Data Science is a field that values skills and experience over age, and many professionals transition into it later in their careers. Gaining relevant knowledge in programming, statistics, and environmental science can be achieved at any age, and employers often prioritize expertise and problem-solving ability over age-related factors.

What are some common challenges faced by environmental data scientists when working with real-world datasets?

Environmental data scientists often encounter challenges such as incomplete or inconsistent data, varying data formats, and the need to integrate information from multiple sources like sensors, satellites, and field observations. Addressing missing values, data quality issues, and ensuring proper geospatial alignment can be time-consuming but is essential for producing reliable analyses. Collaboration with domain experts and stakeholders is frequently required to interpret findings and ensure that the results are actionable for environmental policy or management decisions.

What is the difference between Environmental Data Science vs Environmental Data Analyst?

AspectEnvironmental Data ScienceEnvironmental Data Analyst
Required CredentialsTypically requires a degree in data science, environmental science, or related fields; often includes programming and statistical certificationsUsually requires a degree in environmental science, geography, or related fields; may include basic data analysis certifications
Work EnvironmentResearch labs, data centers, environmental agencies, or consulting firmsEnvironmental agencies, research organizations, or consulting firms
Employer & Industry UsageUsed in environmental research, climate modeling, and policy analysisUsed in environmental monitoring, reporting, and data interpretation

Environmental Data Science focuses on developing models and algorithms to analyze complex environmental data, often requiring advanced programming skills. In contrast, Environmental Data Analysts primarily interpret and visualize environmental data to support decision-making. Both roles are vital but differ in technical depth and scope.

What is the highest paying environmental science job?

Environmental Data Science roles such as senior environmental data scientists or environmental analytics managers tend to have the highest salaries in the field, often exceeding $100,000 annually. These positions typically require advanced skills in data analysis, programming, and environmental modeling, and may involve leadership responsibilities or specialized expertise in areas like climate modeling or sustainability analytics.

What are the key skills and qualifications needed to thrive as an Environmental Data Scientist, and why are they important?

To thrive as an Environmental Data Scientist, you need strong quantitative skills, expertise in environmental science, and a relevant degree in data science, statistics, or a related field. Familiarity with data analysis tools such as Python, R, GIS software, and experience with large datasets or machine learning techniques is typical. Exceptional problem-solving abilities, communication skills, and attention to detail set top performers apart in this field. These competencies are crucial for effectively interpreting complex environmental data, informing policy, and driving impactful sustainability initiatives.
What are the most commonly searched types of Environmental Data Science jobs in Delaware? The most popular types of Environmental Data Science jobs in Delaware are:
What are popular job titles related to Environmental Data Science jobs in Delaware? For Environmental Data Science jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Environmental Data Science jobs in Delaware look for? The top searched job categories for Environmental Data Science jobs in Delaware are:
Environmental Data Science jobs near you
Infographic showing various Environmental Data Science job openings in Delaware as of June 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 100% In-person job distribution, with an average salary of $122,844 per year, or $59.1 per hour.

Data Scientist Executive Director - Card Data & Analytics, Customer & Strategic Analytics

JPMorganChase

Wilmington, DE • On-site

Full-time

Posted yesterday

Key responsibilities

  • Define and drive the AI strategy for Card Data & Analytics, identifying high-value opportunities for generative AI, agentic AI, and analytics innovation.

  • Partner with Data, Product, and Technology to deliver AI and machine learning solutions from ideation and prototyping through production deployment.

  • Lead, mentor, and develop a multi-layered team of analytics leaders, data scientists, and analysts.

Job description

Job Summary:
JPMorganChase is a leading financial services firm, and they are seeking a Data Scientist Executive Director to lead their Card Data & Analytics Customer & Strategic Analytics team. This role involves driving measurable business impact through AI and advanced analytics, leading a high-performing team, and shaping product strategy and customer experience.
Responsibilities:
• Define and drive the AI strategy for Card Data & Analytics, identifying high-value opportunities for generative AI, agentic AI, and analytics innovation to create competitive advantage
• Partner with Data, Product, and Technology to deliver AI and machine learning solutions from ideation and prototyping through production deployment, ensuring solutions are scalable, responsible, and aligned to business needs
• Stay current on emerging AI and machine learning techniques and translate new capabilities into practical applications for the Card business
• Lead analytics supporting customer experience and benefits, delivering insights that inform customer engagement, product design, portfolio performance, and pricing and targeting strategies
• Partner with Product, Risk, and Finance stakeholders to define analytical priorities, interpret results, and drive data-informed decisions
• Drive measurement frameworks, experimentation (including A/B testing and causal inference), and personalization strategies that improve customer experience and benefits utilization
• Translate customer data into actionable insights that inform marketing, product, and servicing strategies.
• Build and lead competitive intelligence analytics capabilities that monitor market trends, competitor positioning, and industry benchmarks within the Card space
• Partner with external vendors and synthesize internal and external data sources to provide senior leaders a clear view of the competitive landscape
• Deliver forward-looking analyses that inform strategic planning and product roadmap decisions for the Card business
• Lead, mentor, and develop a multi-layered team of analytics leaders, data scientists, and analysts, fostering a high-performance, inclusive, growth-oriented culture.
• Set clear goals and performance expectations, provide ongoing coaching, and support career development across all levels of the team
• Attract and retain top analytics talent through hiring, onboarding, and skills development programs.
• Champion a culture of innovation, intellectual rigor, and collaborative problem-solving across the broader Card Data & Analytics organization.
Qualifications:
Required:
• Master’s or PhD in a quantitative field and 10+ years of analytics experience
• Proven senior leadership experience managing and developing multi-disciplinary analytics teams, including managers and individual contributors, in a large enterprise environment
• Deep expertise in data science and analytics, including hands-on experience with predictive modeling, statistical analysis, segmentation, and experimentation
• Demonstrated ability to deliver AI, machine learning, and analytics solutions that drive measurable business outcomes, ideally within financial services or consumer products
• Strong business acumen with the ability to frame analytical problems in terms of business strategy and translate insights into executive-level recommendations
• Experience leading analytics across multiple concurrent business domains, balancing near-term delivery with longer-term capability building
• Exceptional stakeholder management and communication skills, with a track record of influencing senior leaders and cross-functional partners
• Proficiency in Python and/or R, and experience with modern data platforms such as Snowflake or Databricks
• Strong project and program management skills, including the ability to define success metrics, manage risks, and drive execution across complex initiatives
Preferred:
• Experience in Card, consumer lending, or payments analytics, including familiarity with installment products, credit risk, or customer lifecycle management
• Hands-on experience with generative AI solutions, including large language models, retrieval-augmented generation, and agentic AI frameworks
• Experience building or leading competitive intelligence functions using alternative data, market data, or external benchmarking
• Experience with causal inference, A/B testing, and experimentation frameworks at scale
• 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
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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