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Applied Data Science Jobs in Washington (NOW HIRING)

SteerBridge

Senior Data Scientist

Vienna, VA

... data science framing activities, including but not limited to working with partners to identify ... S. or Ph.D. in Applied Mathematics, Statistics, or a related field; alternatively, a Bachelor ...

Agile Defense

Data Scientist

Fort George G Meade, MD · On-site

Preferred Skills and Experience • 4+ years of experience in applied data science, machine learning engineering, or data pipeline development. • Proficient in Python, SQL, and distributed data ...

SteerBridge

Senior Data Scientist

Vienna, VA · On-site

$161K - $195K/yr

... data science framing activities, including but not limited to working with partners to identify ... MSc or PhD degree in applied mathematics, statistics, or relevant work experience. * An active ...

Agile Defense

Data Scientist

Fort George G Meade, MD · On-site

Preferred Skills and Experience 4+ years of experience in applied data science, machine learning engineering, or data pipeline development. Proficient in Python, SQL, and distributed data frameworks ...

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Applied Data Science information

What are the typical responsibilities of an Applied Data Science professional on a day-to-day basis?

An Applied Data Science professional typically spends their days gathering, cleaning, and analyzing structured and unstructured data to uncover patterns and generate actionable insights. They frequently build and deploy predictive models, collaborate with business and engineering teams to define project requirements, and communicate findings through clear reports or visualizations. Additionally, they often engage in regular team meetings, contribute to ongoing process improvements, and continuously learn new technologies or methodologies to enhance project outcomes. This combination of technical and collaborative work makes the role both dynamic and highly impactful within most organizations.

What is the salary of applied data scientist?

The average salary of an applied data scientist typically ranges from $80,000 to $130,000 annually, depending on experience, location, and industry. Entry-level roles may start lower, while experienced professionals with advanced skills in machine learning and data analysis can earn higher salaries.

What does applied data science do?

Applied data science involves using data analysis, statistical methods, and machine learning techniques to solve real-world problems and inform decision-making. Professionals in this field work with large datasets, programming tools like Python or R, and often collaborate with business teams to develop actionable insights. It requires strong analytical skills and knowledge of data management and modeling.

What can you do with an applied data science degree?

An applied data science degree prepares individuals for roles such as data analyst, data scientist, machine learning engineer, or business intelligence analyst. Graduates can work in industries like finance, healthcare, technology, and marketing, utilizing skills in programming, statistical analysis, and data visualization tools like Python, R, and SQL.

What jobs can I get with applied science?

Applied Data Science prepares individuals for roles such as data analyst, data scientist, machine learning engineer, and business intelligence analyst. These jobs involve analyzing data, building models, and using tools like Python, R, and SQL to support decision-making across various industries.

What are the key skills and qualifications needed to thrive in the Applied Data Science position, and why are they important?

To thrive in Applied Data Science, you need a strong background in statistics, machine learning, data analysis, and programming languages such as Python or R, typically evidenced by a degree in a quantitative field. Familiarity with data visualization tools (like Tableau), cloud platforms (AWS, GCP), and certifications in data science or analytics are highly valued. Effective communication, problem-solving, and teamwork are crucial soft skills to convey insights and collaborate with both technical and non-technical stakeholders. These competencies are critical for transforming complex data into actionable business strategies and driving measurable impact within organizations.

What is an Applied Data Science job?

An Applied Data Science job focuses on using data science techniques to solve real-world problems in business, healthcare, finance, and other industries. It involves collecting, processing, analyzing, and interpreting large datasets to extract meaningful insights. Applied data scientists use machine learning, statistical modeling, and programming skills to develop data-driven solutions. They work closely with stakeholders to implement models that drive decision-making and improve operations.

What are popular job titles related to Applied Data Science jobs in Washington? For Applied Data Science jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Applied Data Science jobs in Washington look for? The top searched job categories for Applied Data Science jobs in Washington are:
What cities in Washington are hiring for Applied Data Science jobs? Cities in Washington with the most Applied Data Science job openings:
Applied Data Science jobs near you
Infographic showing various Applied Data Science job openings in Washington as of June 2026, with employment types broken down into 91% Full Time, 8% Part Time, and 1% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.
Lead Data Scientist - Veterans Affair

Lead Data Scientist - Veterans Affair

ONEGLOBE LLC

Washington, DC

Other

Posted 6 days ago

Job description

Description

  • Leads data science efforts, working closely with clients, data, developers to understand mission and data. 
  • Creates data pipelines and flows to clean and transform data for use in data models and applications.
  • Develops algorithms leveraging modern data science technologies including reinforcement learning systems, machine learning and more. 
  • Deep expertise in causal inference, high-dimensional regression, time-series analysis, and forecasting
  • Strong proficiency in Python or R (PyData stack: Pandas, NumPy, SciPy, Statsmodels, Scikit-learn)
  • Demonstrated ability to communicate statistical findings clearly to non-technical executive audiences
  • Lead development of advanced AI/ML systems using techniques such as deep learning, representation learning, time-series modeling, survival analysis, and probabilistic modeling to solve complex healthcare problems or analyze fraud
  • Ability to analyze and profile varied related and unrelated data sets. 
  • Manage technology projects or lead technology solutioning. 
  • Provides highly technical and specialized guidance and solutions to complex IT problems; performs elaborate analyses and studies. 
  • Evaluates, recommends, and executes new technologies and updates existing infrastructure to ensure optimal performance and efficiency. 
  • Develops IT strategies to ensure the systems meet existing and future requirements based on needs and regulations. 
  • Works in a variety of environments and has excellent verbal and non-verbal communication skills.

Requirements

  •  Must be a U.S. Citizen to obtain a security clearance.
  • BS with 10+ years of experience in machine learning, artificial intelligence, or applied data science with 7+ years of designing and deploying production machine learning systems.
  • 8+ years of experience in Python-based machine learning development using frameworks such as PyTorch, TensorFlow, or equivalent along with solid SQL skills.
  • 5+ years of experience deploying production ML systems including model serving, monitoring, ML lifecycle management, and collaboration with engineering teams.
  • Experience configuring and working in Azure environments to execute data science activities including data preparation, analysis, and model development, training, and deployment. 
  • Experience building and integrating the at the application and database level.
  • At least 2 years of theoretical and practical background in statistical analysis, machine learning, predictive modeling, and/or optimization.
  • Excellent verbal and written communications skills along with the ability to present technical data and approaches to both technical and non-technical audiences. 
  • Ability to work efficiently with a geographically distributed team using virtual collaboration tools.

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