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Data Science Software Engineer Jobs in Washington

Cymertek

Data Scientist / Software Engineer

Reston, VA · On-site

In this role, you will work at the intersection of data science and software engineering, developing models and algorithms that extract actionable insights from large datasets while ensuring the ...

Cymertek

Data Scientist / Software Engineer

Tysons, VA · On-site

In this role, you will work at the intersection of data science and software engineering, developing models and algorithms that extract actionable insights from large datasets while ensuring the ...

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Showing results 1-20

All JobsData Science Software Engineer JobsData Science Software Engineer Jobs in Washington

Data Science Software Engineer information

See Washington salary details

$50.4K

$146.9K

$201K

How much do data science software engineer jobs pay per year?

As of Jul 20, 2026, the average yearly pay for data science software engineer in Washington is $146,916.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,700.00 and $155,700.00 per year, depending on experience, location, and employer.

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

To thrive as a Data Science Software Engineer, you need strong proficiency in programming (especially Python or R), a solid understanding of statistics and algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with machine learning frameworks (such as TensorFlow or scikit-learn), data processing tools (like Spark or Hadoop), and cloud platforms (AWS, GCP, or Azure) is essential, as are relevant certifications. Excellent problem-solving abilities, communication skills, and the ability to work collaboratively with cross-functional teams set top performers apart. These competencies are vital for efficiently developing scalable data-driven solutions that drive business insights and innovation.

How does a Data Science Software Engineer typically collaborate with data scientists and other stakeholders on projects?

Data Science Software Engineers play a vital role in bridging the gap between data science and software engineering teams. They work closely with data scientists to translate prototypes and models into scalable, production-ready code, and often collaborate with product managers, analysts, and infrastructure engineers to ensure seamless integration. Regular communication and code reviews are essential, as is an iterative development process to address feedback and ensure solutions meet both technical and business requirements. This cross-functional collaboration helps deliver robust data-driven applications that align with organizational goals.

Which is the hardest field in it?

For a Data Science Software Engineer, the most challenging fields often involve complex machine learning algorithms, large-scale data processing, and advanced statistical analysis. Staying current with rapidly evolving tools like Python, R, and cloud platforms also requires continuous learning and adaptation. These areas demand strong problem-solving skills and deep technical knowledge.

What is a Data Science Software Engineer?

A Data Science Software Engineer is a professional who combines software engineering skills with data science expertise to build scalable data-driven systems and applications. They design, develop, and optimize software that supports data pipelines, machine learning models, and analytics platforms. Their work bridges the gap between data scientists, who focus on statistical analysis and modeling, and traditional software engineers, who focus on building robust and efficient software systems. Data Science Software Engineers ensure that data solutions are production-ready, scalable, and maintainable.

Can a software engineer work as a data scientist?

A software engineer can transition to a data scientist role by developing skills in statistics, machine learning, and data analysis, often using tools like Python, R, and SQL. While the roles have different focuses, software engineers' programming expertise can be a strong foundation for data science work, especially with additional training or experience in data modeling and analytics.

Is 40 too late for data science?

Data science software engineers can enter the field at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and tools like Python or R. Age is not a barrier if you develop the necessary technical expertise and stay current with industry trends.

What engineers make $500,000?

Senior data science software engineers with extensive experience, advanced skills in machine learning, and proficiency in tools like Python, R, and cloud platforms can reach salaries of $500,000 or more, especially in high-cost-of-living areas or within large tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What is the difference between Data Science Software Engineer vs Data Analyst?

AspectData Science Software EngineerData Analyst
Required SkillsProgramming, software development, machine learningData visualization, statistical analysis, reporting
Work EnvironmentSoftware development teams, engineering projectsBusiness units, reporting teams
Common ToolsPython, Java, SQL, ML frameworksExcel, Tableau, SQL, R
Industry UsageTech, finance, healthcare, startupsMarketing, finance, retail, research

While both roles analyze data, Data Science Software Engineers focus on developing software solutions and machine learning models, requiring strong programming skills. Data Analysts primarily interpret data through visualization and statistical methods to support business decisions. The roles often overlap but serve different functions within organizations.

What are popular job titles related to Data Science Software Engineer jobs in Washington? For Data Science Software Engineer jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Data Science Software Engineer jobs in Washington look for? The top searched job categories for Data Science Software Engineer jobs in Washington are:
What cities in Washington are hiring for Data Science Software Engineer jobs? Cities in Washington with the most Data Science Software Engineer job openings:
Data Science Software Engineer jobs near you
Infographic showing various Data Science Software Engineer job openings in Washington as of July 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $146,916 per year, or $70.6 per hour.
Data Science Software Engineer with Security Clearance

Data Science Software Engineer with Security Clearance

Quantum Science Solutions

Laurel, MD • On-site

$114K - $137K/yr

Other

Re-posted 3 days ago

Job description

Opportunity via QSSHire | Talent-as-a-Service Recruiting Role: Data Science Software Engineer Location: Laurel, MD Clearance: TS/SCI with Full Scope Polygraph Rate: Open Are You a Software Engineer Who Enjoys Building Advanced Analytics and AI Solutions? QSSHire is seeking an experienced Data Science Software Engineer to support the development, enhancement, and modernization of compliance analytics and business process automation capabilities. The ideal candidate will leverage software engineering, machine learning, and data science expertise to build scalable analytics solutions, develop AI/ML proof-of-concepts, and improve operational efficiency through automation and advanced data processing techniques. This role offers the opportunity to work on cutting-edge machine learning initiatives, modern data architectures, and emerging AI technologies in a highly collaborative environment. In This Role, You'll: • Design, develop, and enhance analytics applications and compliance automation tools. • Build, train, deploy, and monitor machine learning models supporting mission and business objectives. • Research, evaluate, and integrate emerging AI and machine learning technologies. • Develop proof-of-concept solutions and prototypes to support modernization initiatives. • Engineer scalable data processing workflows and analytics pipelines. • Perform feature engineering and model optimization for machine learning applications, including Random Forest models. • Develop software solutions using Java, Scala, and Python. • Work with structured and unstructured data using SQL, JSON, Lucene, and JEXL. • Implement metrics, monitoring, and observability capabilities for applications, models, and data flows. • Collaborate with technical teams and stakeholders to document requirements, designs, and operational procedures. • Participate in code reviews, testing activities, troubleshooting efforts, and continuous improvement initiatives. • Utilize Agile development methodologies and modern software engineering practices throughout the development lifecycle. You'll Succeed Here If You Have: • 20+ years of software engineering experience supporting programs of similar scope and complexity. • Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline. Four additional years of software engineering experience may be substituted for a degree. • Strong experience developing applications with: Java, Scala, Python • Experience with: Linux development environments, Lucene, JEXL, SQL, JSON, Git/GitLab, Stash, or Bitbucket, Jira, Confluence, Jupyter Notebooks, IntelliJ and/or Eclipse • Experience developing, deploying, and monitoring machine learning models. • Experience performing feature engineering and working with Random Forest algorithms. • Experience with Scikit-learn. • Experience with distributed processing technologies such as MapReduce/Ghostmachine. • Strong analytical, problem-solving, and technical documentation skills. • Ability to work independently and collaboratively within cross-functional teams. • Excellent verbal and written communication skills. Bonus Points If You Have: • Data Science education or professional experience. • Experience implementing production Machine Learning systems. • Experience with AWS cloud services. • Experience with Apache Spark. • Experience performing Exploratory Data Analysis (EDA). • Experience developing machine learning feature pipelines. • Experience with Agentic AI frameworks and architectures. • Experience working with Large Language Models (LLMs). • Experience evaluating and implementing advanced machine learning algorithms and techniques. • Experience supporting compliance, governance, or enterprise analytics initiatives. Why QSSHire? As a modern Talent-as-a-Service recruiting partner, QSSHire connects top technical professionals with meaningful opportunities supporting critical government missions. We focus on transparency, career growth, and helping talented individuals make a lasting impact through innovative technology solutions.

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