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Data Science Jobs in Delaware (NOW HIRING)

Data Analyst

Lewes, DE · On-site +1

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or related field. * Proficiency in data analysis tools such as SQL, Excel, Python, or R. * Experience with data ...

Bachelor's degree in health informatics, Data Science, Statistics, Public Health or a related field. * Minimum of 2-3 of Data Analysis experience in a healthcare environment Language Skills:

... Data Science, Engineering, and Analytics to develop strategies to optimize training data for machine learning models • Lead efforts to identify patterns and trends in conversational data through ...

Sr Data Analyst

Wilmington, DE · Hybrid

$83K - $105K/yr

Bachelor's degree in Data Science/Analytics, Computer Science, Finance, Economics, Accounting or related field. Minimum Years of Experience * 4 years' experience in a experience as a Data Analyst or ...

Showing results 41-60

Data Science information

See Delaware salary details

$37.5K

$122.8K

$196.7K

How much do data science jobs pay per year?

As of Aug 12, 2026, the average yearly pay for 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.

Is a data scientist in high demand?

Yes, data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What are the key skills and qualifications needed to thrive as a data scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

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

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
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 Data Science jobs in Delaware? For Data Science jobs in Delaware, the most frequently searched job titles are:
What cities in Delaware are hiring for Data Science jobs? Cities in Delaware with the most Data Science job openings:
Infographic showing various Data Science job openings in Delaware as of August 2026, with employment types broken down into 85% Full Time, and 15% Part Time. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $122,844 per year, or $59.1 per hour.

Data Analyst

Winaxis LLC

Lewes, DE • On-site, Remote

Full-time

Re-posted 29 days ago


Job description

Description

Join Winaxis LLC as a Data Analyst and play a crucial role in analyzing and interpreting complex data sets to drive key business decisions. As a Data Analyst at Winaxis LLC, you will be responsible for extracting, transforming, and presenting data in a meaningful way to support various projects and initiatives. This role is essential in providing valuable insights that will directly impact the company's strategic direction and growth.

As a Data Analyst at Winaxis LLC, you will work on a variety of projects ranging from market research and trend analysis to performance tracking and forecasting. Your analytical skills will be put to the test as you uncover patterns, trends, and correlations within the data to help optimize processes and improve overall business performance. This position offers a great opportunity to work remotely and collaborate with cross-functional teams to drive data-informed decisions.

Responsibilities:
  • Collect, clean, and validate data to ensure accuracy and reliability.
  • Analyze large datasets using statistical methods and data visualization techniques.
  • Interpret data and present findings to key stakeholders in a clear and concise manner.
  • Develop and maintain reports, dashboards, and data visualizations to track KPIs.
  • Identify trends, patterns, and insights to support business objectives.
  • Collaborate with teams to define data requirements for various projects.
  • Conduct ad-hoc analysis to address specific business questions and challenges.
  • Stay updated on industry trends and best practices in data analysis.
Requirements:
  • Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or related field.
  • Proficiency in data analysis tools such as SQL, Excel, Python, or R.
  • Experience with data visualization tools like Tableau, Power BI, or similar.
  • Strong analytical and problem-solving skills with attention to detail.
  • Ability to work independently and collaborate effectively in a team environment.
  • Excellent communication skills to convey complex findings to non-technical stakeholders.
  • Knowledge of statistical methods and techniques for data analysis.
  • Experience in interpreting and analyzing large datasets to extract insights.