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Data Science Analytics Jobs in Washington (NOW HIRING)

Ampcus Inc

Senior Data Scientist

Chantilly, VA

This role combines data science, analytics engineering, artificial intelligence, and software development to: (1) Establish AI Program metrics--from conceptual definition through technical ...

Analytic Services Inc

Data Scientist

Washington, DC · On-site

You will leverage data science, machine learning, and statistical techniques to analyze, model, and visualize complex multi-INT datasets, delivering actionable insights that support OBI analytic ...

Analytic Services Inc

Data Scientist

Washington, DC · On-site

You will leverage data science, machine learning, and statistical techniques to analyze, model, and visualize complex multi-INT datasets, delivering actionable insights that support OBI analytic ...

Elder Research

Data Scientist

Arlington, VA · On-site

Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science-driven solutions in support of assessments, gap analyses, and actionable ...

Elder Research

Data Scientist

Arlington, VA

Explore, clean, and wrangle large, complex datasets to uncover insights and identify opportunities for data science-driven solutions in support of assessments, gap analyses, and actionable ...

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All JobsData Science Analytics JobsData Science Analytics Jobs in Washington

Data Science Analytics information

See Washington salary details

$27

$62

$106

How much do data science analytics jobs pay per hour?

As of Jun 11, 2026, the average hourly pay for data science analytics in Washington is $62.01, according to ZipRecruiter salary data. Most workers in this role earn between $49.81 and $70.24 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Science Analytics professional, and why are they important?

To thrive in Data Science Analytics, a strong background in statistics, data modeling, and programming (often with a degree in computer science, mathematics, or a related field) is essential. Familiarity with tools such as Python, R, SQL, and data visualization platforms like Tableau or Power BI, as well as knowledge of machine learning libraries, is typically required. Critical thinking, problem-solving, and effective communication skills help professionals translate complex data insights into actionable business strategies. These competencies are crucial for extracting meaningful information from data and driving informed decision-making within organizations.

Is AI replacing data analysts?

AI is transforming the role of data analysts by automating routine tasks such as data cleaning and basic analysis, allowing analysts to focus on more complex insights and strategic decision-making. While AI tools can augment their work, human expertise remains essential for interpreting results, understanding context, and making nuanced judgments. Data analysts with skills in machine learning, programming, and data visualization are increasingly valuable in this evolving environment.

How do data science analytics professionals typically collaborate with other departments within an organization?

Data science analytics professionals often work closely with teams across the organization, such as marketing, finance, product development, and IT. Their role involves understanding business needs, gathering requirements, and translating complex data findings into actionable insights for non-technical stakeholders. Effective communication and teamwork are essential, as data scientists may participate in cross-functional meetings, present their analyses, and tailor their recommendations to support strategic decision-making. This collaborative approach not only enhances the impact of analytics projects but also fosters continuous learning and innovation within the organization.

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

AspectData Science AnalyticsData Analyst
Required CredentialsDegree in Data Science, Statistics, or related fields; programming skillsDegree in Statistics, Mathematics, or related fields; proficiency in Excel and SQL
Work EnvironmentOften involves complex modeling, machine learning, and predictive analyticsFocuses on data cleaning, reporting, and visualization
Employer & Industry UsageTech companies, finance, healthcare, and research institutionsBusiness, marketing, finance, and operations across various industries

Data Science Analytics and Data Analysts both work with data, but Data Science Analytics typically involves advanced modeling and predictive techniques, while Data Analysts focus on data reporting and visualization. The roles often overlap, but Data Science Analytics requires more technical skills and a deeper understanding of algorithms.

What is the job of data science and analytics?

Data science and analytics involve collecting, processing, and analyzing large datasets to extract meaningful insights that support decision-making. Professionals in this field use statistical methods, programming tools like Python or R, and visualization techniques to identify trends, solve problems, and improve business outcomes.

What is data science analytics?

Data science analytics is the process of extracting insights and knowledge from data using statistical, mathematical, and computational techniques. It involves collecting, cleaning, analyzing, and visualizing data to help organizations make informed decisions. Professionals in this field use tools like Python, R, and SQL to interpret complex data sets, build predictive models, and identify trends or patterns. Data science analytics plays a key role in industries such as finance, healthcare, retail, and technology, enabling businesses to optimize operations and improve outcomes.

Is 40 too late for data science?

Data science analysts and professionals can enter the field at any age, including 40 or older. Success depends on acquiring relevant skills such as programming, statistics, and tools like Python or R, as well as gaining experience through projects or certifications. Age is less important than skills, continuous learning, and adapting to industry changes.

What jobs can you get with data science and analytics?

Data science and analytics skills open opportunities for roles such as data analyst, data scientist, business intelligence analyst, machine learning engineer, and data engineer. These positions typically require proficiency in programming languages like Python or R, statistical analysis, and data visualization tools, often within technology, finance, healthcare, or marketing industries.
What are the most commonly searched types of Data Science Analytics jobs in Washington? The most popular types of Data Science Analytics jobs in Washington are:
What are popular job titles related to Data Science Analytics jobs in Washington? For Data Science Analytics jobs in Washington, the most frequently searched job titles are:
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Data Science Analytics jobs near you
Pega Data Science & Analytics

Pega Data Science & Analytics

INSPYR Solutions

Merrifield, VA • On-site

Full-time

Medical, Retirement

Posted yesterday

Job description

Title: Pega Data Science & Analytics
Location: Vienna, VA, Winchester, VA or Pensacola, FL (HYBRID)
Duration: 6-9 months initial duration with possible extension
Work Requirements: US Citizen or GC Holders
Pega Data Science & Analytics
To enable and simplify analysis required for faster implementation of new CDH modeling features
Prioritized Deliverables
  1. Library of required queries/scripts to replicate the CDH customer contextual object in external systems (databricks/asl) for deeper analysis
  2. Standardize format for executing key data retrieval steps for use by the broader team
    a. Interaction to outcome attribution (account opens)
    b. Model data to interaction mapping (model performance, predictor performance)
    c. Member Profile to interaction mapping
  3. Create notebooks for the broader team to use to answer specific questions
    a. Distribution Analysis
    b. Arbitration Analysis
    c. Channel Engagement Analysis

Skillset
The primary technical skills required would be familiarity with the databricks environment and proficiency with Python/PySpark and SQL. Pega CDH experience is preferred.
Some examples of the work as it directly relates to GEM:
  • Initial Analysis to Support New Model Related Features
    • Propensity Thresholds
      • Creating the back-testing approach (MDSA had no appetite at the time)
      • Establishing baseline KPIs
      • Creating the monitoring approach
    • Initial Model Maturity Analysis
      • Establishing baseline KPIs
      • Gauging the impact of enabling the feature
      • Creating the ongoing monitoring approach
  • On-going Analysis
    • Model Performance Monitoring
      • Though MDSA owns the code to run the notebooks, when changes must be made to the code GEM is heavily involved in creating the new logic
    • NBI Program Model Health
      • This exists in some form today, but it is not in a state that is readily available to be shared with leaders in O&A, MDSA, or broader Marketing

Broader O&A Analytical Gaps
  • "Actionable Monitoring Data:" Standardizing how we conduct this sort of analysis for consistency
    • Capture when propensity scores are exceptionally low closer to real-time (1 day)
    • Capture when actions are not providing value to their intended objective (acquisition, engagement)
  • Eligible Audience Monitoring
    • Identifying Members eligible for different actions/treatments (simulation environment can help after going live to a certain extent)
    • Tying interactions back to key Member demographic data for more granular analysis (this sort of analysis should be standardized so it can easily be done by all Members of O&A)

Our benefits package includes:
  • Comprehensive medical benefits
  • Competitive pay
  • 401(k) retirement plan
  • ...and much more!

About INSPYR Solutions
Technology is our focus and quality is our commitment. As a national expert in delivering flexible technology and talent solutions, we strategically align industry and technical expertise with our clients' business objectives and cultural needs. Our solutions are tailored to each client and include a wide variety of professional services, project, and talent solutions. By always striving for excellence and focusing on the human aspect of our business, we work seamlessly with our talent and clients to match the right solutions to the right opportunities. Learn more about us at inspyrsolutions.com.
INSPYR Solutions provides Equal Employment Opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. In addition to federal law requirements, INSPYR Solutions complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities.
Information collected and processed through your application with INSPYR Solutions (including any job applications you choose to submit) is subject to INSPYR Solutions' Privacy Policy and INSPYR Solutions' AI and Automated Employment Decision Tool Policy: https://www.inspyrsolutions.com/policies/ . By submitting an application, you are consenting to being contacted by INSPYR Solutions through phone, email, or text.
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