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Remote Data Science Jobs in Delaware (NOW HIRING)

Mondo

Technical Product Manager

Wilmington, DE · Remote

$80 - $90/hr

Remote (East Coast candidates preferred) Start Date: ASAP Duration: 6-12 Month Contract ... Build and maintain a clear understanding of current-state architecture, data flows, and operational ...

Agilent

R&D Software Architect

Wilmington, DE · On-site +1

$110K - $194K/yr

You will work in a globally distributed, fully remote team building SaaS platforms, analytics, and ... scientific instruments or other regulated enterprise environments. * Designing analytics or data ...

SSP International Inc.

Program Manager

Wilmington, DE · On-site +1

$65K - $68K/yr

Remote, United States Travel required: Approximately 30 days Salary: $65,000 - $68,000 Summary SSP ... Since 1959, SSPI's flagship program, the Summer Science Program, has offered immersive summer ...

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All JobsRemote Data Science JobsRemote Data Science Jobs in Delaware

Remote Data Science information

See Delaware salary details

$22.1K

$98.2K

$187.4K

How much do remote data science jobs pay per year?

As of Jun 19, 2026, the average yearly pay for remote data science in Delaware is $98,181.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,715.00 and $136,180.00 per year, depending on experience, location, and employer.

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

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

Can a data scientist work fully remote?

Yes, many data scientists work fully remote, especially in companies that prioritize flexible work arrangements. Remote data science roles often require strong communication skills, proficiency with collaboration tools, and the ability to work independently on projects using programming languages like Python or R. However, some positions may require occasional in-person meetings or on-site presence depending on company policies.

Is 40 too late for data science?

Age is not a barrier to entering data science, and many professionals start or transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications, regardless of age.

Will AI replace data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not eliminate the need for human expertise in interpreting results, designing models, and making strategic decisions. Data scientists will continue to be essential for developing complex algorithms, understanding business context, and ensuring ethical use of AI tools. Skills in programming, statistical analysis, and machine learning remain critical for the profession's evolving landscape.

What Are the Qualifications to Get a Remote Data Science Job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

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

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables, optimize models, and prioritize tasks for efficiency.
More about Remote Data Science jobs
What are the most commonly searched types of Data Science jobs in Delaware? The most popular types of Data Science jobs in Delaware are:
What are popular job titles related to Remote Data Science jobs in Delaware? For Remote Data Science jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Remote Data Science jobs in Delaware look for? The top searched job categories for Remote Data Science jobs in Delaware are:
What cities in Delaware are hiring for Remote Data Science jobs? Cities in Delaware with the most Remote Data Science job openings:
Remote Data Science jobs near you
Technical Product Manager

Technical Product Manager

Mondo

Wilmington, DE • Remote

$80 - $90/hr

Contractor

Medical, Dental, Vision, Retirement

Posted 14 days ago

Be an early applicant

Job description

Apply now: Technical Product Manager, Remote (East Coast). Start date is ASAP for this contract position.
Job Title: Technical Product Manager
Location-Type: Remote (East Coast candidates preferred)
Start Date: ASAP
Duration: 6-12 Month Contract
Compensation Range: $80/hr - $90/hr on W2
Benefits: Eligible for Health, Dental, Vision, and 401K
Visa Sponsorship: Not eligible for visa sponsorship
Job Description:
The client is seeking a Technical Product Manager to lead a structured, service-by-service modernization of core backend services, bridging business objectives and engineering execution to drive a scalable, cloud-forward architecture.
Job Summary
Own prioritization and trade-offs for the cross-team technical roadmap in alignment with product and engineering leadership, ensuring high-priority work is clearly defined and sequenced for delivery.
Partner with engineering managers and tech leads to shape, document, and refine technical requirements, capturing architectural decisions, constraints, and cross-team contracts.
Proactively identify, track, and manage cross-team dependencies, surfacing trade-offs and reshaping sequencing to enable independent, on-time delivery.
Decompose complex initiatives into outcome-oriented, sprint-sized increments that deliver observable and measurable business value.
Serve as the product advocate for initiatives, translating business drivers to technical stakeholders and technical considerations to non-technical stakeholders.
Build and maintain a clear understanding of current-state architecture, data flows, and operational constraints as the foundation for forward-looking modernization strategy.
Partner with architecture and engineering leadership to identify opportunities to simplify, decouple, and evolve services toward a modular, scalable, and maintainable ecosystem.
Minimum Requirements:
4 years of experience as a Product Manager, Technical Product Manager, Technical Program Manager, or in a comparable role such as Developer or Business Analyst with equivalent responsibilities.
Demonstrated technical fluency across system architecture, data pipelines, microservices, and contract design, with the ability to engage in technical trade-off discussions without owning implementation.
Hands-on experience with API design and integration architecture, including REST APIs, GraphQL, and complex data flows.
Experience leading or contributing to legacy modernization or replatforming initiatives, including cloud migration from mainframe or legacy environments.
Proven ability to map and manage dependencies in complex, multi-team environments using tools such as Jira or Confluence.
Strong communication skills with the ability to translate between technical constraints and business impact across diverse stakeholder groups.
Preferred Qualifications:
Experience in the financial services or lending industry.
Background in software engineering or systems architecture.
Familiarity with containerization and cloud infrastructure, including technologies such as PostgreSQL, Redis, Docker, Kubernetes, and AWS or Google Cloud platforms.
Experience with iterative greenfield development and architecture modernization projects.
Bachelor's degree in Computer Science, Engineering, or equivalent experience.