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Head Data Science Jobs in Delaware (NOW HIRING)

Reporting to the Head of Data Science, this is a critical leadership position that requires key characteristics including the ability to partner and influence across stakeholders, apply strategic ...

Reporting to the Head of Data Science, this is a critical leadership position that requires key characteristics including the ability to partner and influence across stakeholders, apply strategic ...

Senior AI Engineer

Wilmington, DE · On-site +1

$101K - $139K/yr

This is a remote role and will report directly to the Head of Data Science & AI. The role willidentify,solution, build, and launch GenAI products & traditional apps in all areas of the company ...

Science Teacher - SY25/26

Dover, DE · On-site

$48K - $62K/yr

Responsibilities Under the supervision of the Head of School, the General Education Teacher (in and ... Analyze assessment data to identify areas of strength and areas for growth and adjust instructional ...

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Head Data Science information

See Delaware salary details

$21.1K

$103.4K

$191.6K

How much do head data science jobs pay per year?

As of Aug 29, 2026, the average yearly pay for head data science in Delaware is $103,425.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,350.00 and $139,937.00 per year, depending on experience, location, and employer.

What does a head data science do?

A Head of Data Science is responsible for leading and managing the data science team within an organization. They oversee the development and implementation of data-driven strategies, ensuring that the team delivers valuable insights and predictive models to support business goals. This role involves collaborating with other departments, setting the vision for data initiatives, and ensuring best practices in data analysis and machine learning are followed. Additionally, the Head of Data Science often mentors team members and helps shape the organization's overall data strategy.

What are the key skills and qualifications needed to thrive as a head data science?

To thrive as a Head of Data Science, you need advanced expertise in statistics, machine learning, data modeling, and a strong background in computer science or a related quantitative field, often supported by a master's or Ph.D. Proficiency with programming languages like Python or R, big data platforms such as Hadoop or Spark, and familiarity with cloud-based analytics tools are typically required. Strategic leadership, excellent communication skills, and the ability to mentor and inspire teams are crucial soft skills for this role. These abilities are essential to drive data-driven decision-making, foster innovation, and align analytics initiatives with organizational goals.

What are some common challenges faced by a head data science when building and leading a data science team?

As a Head of Data Science, one of the main challenges is balancing strategic leadership with hands-on technical guidance. You'll often need to align the team's goals with broader business objectives while ensuring that team members have the right mix of skills and resources. Additionally, fostering effective collaboration between data scientists, engineers, and business stakeholders can be complex, especially in cross-functional environments. Managing expectations around project timelines and communicating technical insights in a clear, actionable way are also key aspects of the role.

What is the difference between Head Data Science vs Data Science Manager?

AspectHead Data ScienceData Science Manager
ResponsibilitiesStrategic leadership, setting data science vision, overseeing multiple teamsTeam management, project delivery, coordinating data science projects
Required SkillsAdvanced analytics, leadership, strategic planningTeam management, technical expertise, project management
ExperienceSenior data science background, leadership rolesData science experience with managerial responsibilities
Work EnvironmentExecutive level, cross-departmental collaborationTeam-focused, project-oriented

The Head Data Science typically holds a strategic, leadership role overseeing the entire data science function, while the Data Science Manager focuses on managing teams and project execution. Both roles require strong technical backgrounds, but the Head Data Science emphasizes vision and strategy, whereas the Data Science Manager concentrates on operational management.

What are the most commonly searched types of Data Science jobs in Delaware?

The most popular types of Data Science jobs in Delaware are:

Infographic showing various Head Data Science job openings in Delaware as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $103,425 per year, or $49.7 per hour.

Full-time

Posted 11 days ago


OneMain Financial rating

7.4

Company rating: 7.4 out of 10

Based on 102 frontline employees who took The Breakroom Quiz

119th of 152 rated financial services


Job description

The Role

We're seeking a data science leader and practitioner to dream, design, and build machine learning foundations to drive our exciting growth agenda. The VP/MD of Data Science will focus on building the foundational data science capabilities to enable the integration and growth of our emerging Auto Finance businesses. This role will partner closely with credit, operations, and business leaders of our legacy direct auto and recently acquired indirect auto programs. This leader will set the data science strategy, build a team, and create core data science use cases and capabilities to enable rapid growth.

Reporting to the Head of Data Science, this is a critical leadership position that requires key characteristics including the ability to partner and influence across stakeholders, apply strategic thinking, practical business problem solving, a forward leaning results focus, and team leadership and development. This is a rare and exciting opportunity to own the design and development of advanced machine learning capabilities spanning underwriting, marketing, operations, and risk, in a company which combines the best of FinTech and Banking.

This individual will be charged with both building the future while delivering impact in the present and must have both the skills and orientation to get "hands on" the keyboard, particularly in the early days. This leader must also have a strong model risk management orientation - skilled in using "black box" approaches, while remaining skeptical and continuously challenging the team.

Responsibilities

  • Collaborate, influence, and "bring along" business and technology leaders to maximize the impact of machine learning solutions in the auto finance businesses
  • Design and build the foundational data science practices and develop the decision making algorithms (using traditional regression models and machine learning algorithms) to enable rapid and sustained growth of auto finance lending
  • Research and develop new modeling techniques that will keep OneMain at the forefront of the industry
  • Research and develop with both traditional and non-traditional data sources (such as information from other industries, tax data, deposit data, demographic data, digital data) to find insights which can be used in models to keep at the forefront of the industry
  • Apply diverse statistical and machine learning techniques to analyze a variety of datasets to solve complex, unstructured business problems
  • Work with wide range of open-source tools to analyze and extract value from granular tradeline level granular data
  • Recruit, develop, and lead a high performing and high potential team of data scientists to conduct statistical analysis and develop profit-driven decision framework and risk strategies
  • Independently complete analyses, draw conclusions, and present results to all levels of leaders in order to influence business/strategy decision
  • Partner with technology leaders to build and evolve an advanced machine learning environment for auto finance
  • Effectively prioritize competing initiatives across direct reports; translate strategic priorities into individual/team initiatives and manage expectations
  • Collaborate with Model Risk, Legal, Compliance, Audit, and other risk management functions to ensure solutions are developed that meet expected standards and policies

Qualifications

  • Master's Degree in a quantitative discipline (Engineering, Statistics, Economics, Biostatistics, Physics, Computer Science, or related field); PhD preferred
  • 6+ years of progressively broader roles in model development and implementation experience; at least 4 of those years in consumer lending (credit card; unsecured loans; auto lending; or home equity); auto finance experience is a plus, but an enthusiasm and ability to rapidly learn a consumer lending asset class is more important
  • Expert experience designing, developing, and implementing machine learning solutions - must have experience developing and maintaining credit underwriting machine learning systems in a FCRA governed context
  • Applied experience in a variety of modeling techniques like bagging, boosting, NLP and so forth, as was feature engineering
  • Proven experience in communicating with stakeholders across all phases of machine learning modeling project, including validation, deployment, and ongoing monitoring
  • Proven experience in assessing, mentoring, and developing junior data science talent
  • Advanced experience in use of open-source machine learning libraries (Python or R) in a cloud-based environment
  • Experience in identifying and managing the risks of machine learning systems
  • Innovative and capable of developing creative solutions to complex, data-driven problems

OneMain Holdings, Inc. is an Equal Employment Opportunity (EEO) employer. Qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship status, color, creed, culture, disability, ethnicity, gender, gender identity or expression, genetic information or history, marital status, military status, national origin, nationality, pregnancy, race, religion, sex, sexual orientation, socioeconomic status, transgender or on any other basis protected by law.


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