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Data Scientist Machine Learning

Santa Monica, CA · On-site

$75K - $100K/yr

As a Data Scientist Machine Learning, you will work within a small data science team focusing on ... Perform feature engineering to enhance model performance * Select appropriate algorithms based on ...

Merican

Data Science & Machine Learning Engineer

$117K - $140K/yr

Senior Data Science & Machine Learning Engineer Location: Remote, USA (Client Location ZIP: 01730) Duration: 6 Months Contract to Hire We are seeking an experienced Senior Data Science & Machine ...

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Data Scientist Machine Learning Engineer information

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$46K

$165K

$243.5K

How much do data scientist machine learning engineer jobs pay per year?

As of Aug 4, 2026, the average yearly pay for data scientist machine learning engineer in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is the difference between Data Scientist Machine Learning Engineer vs Data Analyst?

AspectData Scientist Machine Learning EngineerData Analyst
Required CredentialsDegree in CS, Data Science, or related; experience with ML frameworksDegree in Statistics, Math, or related; proficiency in data visualization tools
Work EnvironmentDevelops ML models, algorithms, and scalable solutionsAnalyzes data, creates reports, and visualizations
Industry UsageTech, finance, healthcare, and more; focus on predictive modelingBusiness, marketing, finance; focus on reporting and insights

While Data Scientists Machine Learning Engineers focus on building and deploying machine learning models, Data Analysts primarily interpret data through reports and visualizations. Both roles require strong analytical skills, but Data Scientists Machine Learning Engineers typically have more technical expertise in algorithms and coding, making them more involved in model development and deployment.

What are the key skills and qualifications needed to thrive as a data scientist machine learning engineer?

To thrive as a Data Scientist Machine Learning Engineer, a strong background in statistics, programming (Python, R), and machine learning algorithms is essential, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with big data platforms (Spark, Hadoop) and cloud services (AWS, Azure) are commonly required. Strong problem-solving abilities, communication skills, and a collaborative mindset help professionals translate complex data insights into actionable business solutions. These skills are crucial for effectively designing, deploying, and explaining machine learning models that drive innovation and informed decision-making.

What is a data scientist machine learning engineer?

A Data Scientist Machine Learning Engineer is a professional who combines expertise in data analysis, statistical modeling, and software engineering to design, build, and deploy machine learning models. They work with large datasets to extract insights and solve complex problems by developing algorithms and predictive models. In addition to building models, they are responsible for ensuring models are scalable, robust, and integrated into production systems, often collaborating with data engineers and business stakeholders.

How do data scientist machine learning engineers typically collaborate with other departments within an organization?

Data Scientist Machine Learning Engineers often work closely with cross-functional teams such as software engineers, product managers, and domain experts. They collaborate to understand business requirements, gather and preprocess data, and integrate machine learning models into production systems. Regular communication is essential to ensure that developed solutions align with organizational goals and are scalable. This collaborative environment not only helps in building robust models but also enhances the engineer’s understanding of real-world business challenges.

Can a data scientist work as a machine learning engineer?

A data scientist can transition to a machine learning engineer role since both involve working with data and algorithms; however, machine learning engineers typically require stronger software engineering skills, experience with deployment, and knowledge of tools like cloud platforms and APIs. Gaining expertise in production environments and coding in languages such as Python or Java is often necessary. Certifications or training in machine learning engineering can also facilitate this transition.
More about Data Scientist Machine Learning Engineer jobs
What job categories do people searching Data Scientist Machine Learning Engineer jobs look for? The top searched job categories for Data Scientist Machine Learning Engineer jobs are:
Data Scientist Machine Learning Engineer jobs near you
Infographic showing various Data Scientist Machine Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Senior Data Scientist / Machine Learning Engineer

Waypoint Human Capital

Mclean, VA

Full-time

Posted 5 days ago

Job description

Position Title: Senior Data Scientist / Machine Learning Engineer
Position Type: Full-Time, On-Site
Position Location: Tysons, VA
Clearance Required: Active TS/SCI with CI Polygraph or Full Scope Polygraph
Waypoint's client is seeking a dynamic Senior Data Scientist / Machine Learning Engineer with an active TS/SCI CI Poly or higher to join their team. The Senior Data Scientist / Machine Learning Engineer will work directly with data scientists, software engineers, and subject matter experts in the definition of new analytics capabilities able to provide federal customers with the information they need to make proper decisions and enable their digital transformation.
This position works directly with data scientists, software engineers, and subject matter experts to research, design, and deploy machine learning algorithms that support federal customers in digital transformation and data-driven decision making. They will contribute to new analytics capabilities and assist customers in building their own applications. This position requires a bachelor's degree in Computer Science, Electrical Engineering, Statistics, or a related field, 5 to 10 years of relevant experience, and strong Python and applied ML skills. An active TS/SCI with CI Polygraph or Full Scope Polygraph is required.
Responsibilities
The responsibilities include, but are not limited to:
  • Research, design, implement, and deploy Machine Learning algorithms for enterprise applications.
  • Assist and enable federal customers to build their own applications.
  • Contribute to the design and implementation of new features.

Required
  • Active Top Secret clearance with CI Polygraph or Full Scope Polygraph.
  • Bachelor's degree in Computer Science, Electrical Engineering, Statistics, or equivalent fields required.
  • MS or PhD in Computer Science, Electrical Engineering, Statistics, or equivalent fields preferred.
  • Minimum 5–10 years relevant work experience preferred.
  • Excellent programming skills in Python.
  • Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
  • Strong mathematical background (linear algebra, calculus, probability, and statistics).
  • Experience with scalable Machine Learning (MapReduce, streaming).
  • Ability to drive a project and work both independently and in a team.
  • Smart, motivated, can-do attitude, and seeks to make a difference.
  • Excellent verbal and written communication skills.
  • Passion for developing team-oriented solutions to complex engineering problems.
  • Thrive in an autonomous, empowering, and exciting environment.
  • Ability to collaborate across multiple functional teams to improve scalability.
  • Ability to convey highly technical concepts and information in written form to both technical and non-technical audiences.
  • Ability to work on multiple concurrent projects.
  • Strong self-motivation and the ability to work with minimal supervision.
  • Team-oriented, energetic, results- and delivery-focused, with a strong commitment to quality and meeting deadlines.
  • Ability to work in an Agile environment.

Desired
  • Hands-on experience deploying and operating applications using IaaS and PaaS on major cloud providers, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
  • Proficient in leveraging modern LLM tools to accelerate development workflows and enhance code quality.
  • Experience with deep learning.
  • Experience with natural language processing (NLP).
  • Experience with computer vision.
  • Experience with reinforcement learning.