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Data Science Software Engineer Jobs (NOW HIRING)

GRVTY

Data Science Software Engineer

Laurel, MD

$113K - $136K/yr

The Data Science Software Engineer shall develop, enhance, and prototype compliance and business processing analytics and tools. They will also develop new methods for automating compliance functions ...

GRVTY

Data Science Software Engineer

Laurel, MD · On-site

$120K - $175K/yr

The Data Science Software Engineer shall develop, enhance, and prototype compliance and business processing analytics and tools. They will also develop new methods for automating compliance functions ...

GRVTY

Data Science Software Engineer

Laurel, MD

$120K - $175K/yr

The Data Science Software Engineer shall develop, enhance, and prototype compliance and business processing analytics and tools. They will also develop new methods for automating compliance functions ...

Microsoft

Data Engineer

Redmond, WA · On-site

$160K - $261K/yr

Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 3+ years experience in business analytics, data science, software development, data ...

Alutiiq

Software Engineer

San Diego, CA · On-site +1

The Software Engineer will support the Reverse Engineering, Science, and Technology for ... Experience supporting Machine Learning, Computer Vision, or data science workflows. Familiarity ...

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Data Science Software Engineer information

See salary details

$44.5K

$129.7K

$177.5K

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

As of Jun 30, 2026, the average yearly pay for data science software engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 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.

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.

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.

More about Data Science Software Engineer jobs
What cities are hiring for Data Science Software Engineer jobs? Cities with the most Data Science Software Engineer job openings:
What states have the most Data Science Software Engineer jobs? States with the most job openings for Data Science Software Engineer jobs include:
What job categories do people searching Data Science Software Engineer jobs look for? The top searched job categories for Data Science Software Engineer jobs are:
Data Science Software Engineer jobs near you
Infographic showing various Data Science Software Engineer job openings in the United States as of June 2026, with employment types broken down into 2% As Needed, 82% Full Time, 14% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Science Software Engineer

GRVTY

Laurel, MD

$113K - $136K/yr

Other

Posted 25 days ago

Key responsibilities

  • Develop, enhance, and prototype compliance and business processing analytics and tools.

  • Develop new methods for automating compliance functions and improve existing methods.

  • Investigate, integrate, and test compliance modernization Machine Learning/AI algorithms and research proof-of-concepts in the RD environment.

Job description

What You'll be Owning

GRVTY is seeking a with a TS/SCI + Poly clearance (applicable to this customer) to join one of our top projects in .

  • The Data Science Software Engineer shall develop, enhance, and prototype compliance and business processing analytics and tools. They will also develop new methods for automating compliance functions and improve existing methods. Additionally, the Engineer will investigate, integrate, and test compliance modernization Machine Learning/AI algorithms and research proof-of-concepts in the RD environment. Lastly, the Engineer will develop models and implement appropriate metrics and monitoring of developed tools, functions, and data flows.

What You Must Have

  • Active TS/SCI with Polygraph Clearance
  • Twenty (20) years experience as a SWE in programs and contracts of similar scope, type, and complexity is required.
  • Bachelor's degree in Computer Science or related discipline from an accredited college or university is required. Four (4) years of additional SWE experience on projects with similar software processes may be substituted for a bachelor's degree.
  • Experience with Java, Scala, and Python
  • Experience with Lucene, JEXL, SQL, JSON
  • Random Forest and ability to do feature development for Random Forest
  • Experience with Machine Learning Model building and monitoring
  • Experience developing in a Linux operating environment
  • Experience with Ghostmachine (Map/Reduce)
  • Experience with GM Learn
  • Experience with Jupyter Notebooks
  • Experience with IntelliJ and/or Eclipse
  • Experience with Git/Gitlab and/or Stash/Bitbucket
  • Experience with Jira
  • Experience with Confluence
  • Experience with Scikit-learn
  • Agency Compliance standards, policies, and authorities
  • Experience with Agency corporate systems
  • Experience with documentation and reviewing documentation

What Would be Nice to Have

  • Data Science skills/background
  • Experience with AWS
  • Experience implementing ML systems
  • Experience with Spark
  • Exploratory Data Analysis (EDA)
  • Agentic AI
  • Large Language Models (LLM)
  • Machine Learning Feature Development
  • Other Machine Learning models and techniques

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