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Data Scientist Machine Learning Engineer Jobs (NOW HIRING)

AQR

Data Scientist, Machine Learning

Greenwich, CT · On-site

$180K - $200K/yr

You will serve as a bridge between data engineering and quantitative research. Working directly ... for machine learning by developing robust data preparation and quality workflows Your ...

AQR

Data Scientist, Machine Learning

Greenwich, CT · On-site

$180K - $200K/yr

You will serve as a bridge between data engineering and quantitative research. Working directly ... for machine learning by developing robust data preparation and quality workflows . Your ...

TEKsystems

Machine Learning Engineer

Raleigh, NC · On-site

$75 - $85/hr

Top Skills' Details - Masters degree in Computer Science, Machine Learning, Data Science, Engineering, or related discipline. - 10+ years of software engineering, machine learning engineering, or ...

Quince

Senior Data Scientist, Growth

Palo Alto, CA · On-site

Proven background as a Data Scientist, Machine Learning Engineer, or ML Researcher with hands-on experience independently pushing machine learning models into a live production environment. * Deep ...

Azumo

Data Scientist, Applied AI - Remote

Azumo is currently looking for a highly motivated Data Scientist / Machine Learning Engineer to develop and enhance our data and analytics infrastructure. The position is FULLY REMOTE , based in ...

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

Essential Skills * 5+ years of professional experience in Data Science, Machine Learning, Advanced Analytics, Quantitative Research, Data Engineering, or related fields. * Strong proficiency in ...

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Data Scientist Machine Learning Engineer information

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

As of Sep 15, 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 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.

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.

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.

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.

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, algorithms, and statistical models. However, machine learning engineers typically require stronger software engineering skills, experience with deployment, and knowledge of tools like cloud platforms and version control. Gaining expertise in programming, system design, and production environments is often necessary for this transition.
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Infographic showing various Data Scientist Machine Learning Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 12% Part Time, and 2% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.

Senior Data Scientist - Machine Learning & AI

VA • Remote

Team Velocity
Internet and IT • 201 - 500 employees
Learn more about Team Velocityexternal

$140K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 27 days ago

Job description

Senior Data Scientist – Machine Learning & AI
Remote | Full-Time |

Team Velocity is seeking a Senior Data Scientist to develop and deploy machine learning, predictive analytics, and AI solutions that improve customer engagement, marketing performance, operational efficiency, and business intelligence.

This is a hands-on role for an experienced data scientist who can take models from data exploration and development through production deployment, monitoring, and optimization. You will partner with Product, Data Engineering, Software Engineering, Analytics, and business leadership to deliver measurable business impact.

This is a full-time remote position. Candidates must reside in the Continental U.S. and be able to support an 8:30 AM–5:30 PM ET business hours. Eastern and Central Time Zones highly preferred.

KEY RESPONSIBILITIES

  • Design, build, evaluate, and deploy production machine learning models.
  • Develop predictive models for churn, propensity, lead scoring, customer lifetime value, recommendations, forecasting, personalization, and marketing attribution.
  • Perform statistical analysis, hypothesis testing, A/B testing, causal inference, and time-series analysis.
  • Build feature engineering, model training, and inference pipelines.
  • Deploy and monitor ML models, including model performance, drift detection, and retraining.
  • Apply Generative AI, LLMs, RAG, and vector databases to business and customer applications.
  • Partner with Product, Engineering, Analytics, and leadership to translate business problems into scalable data science solutions.
  • Mentor junior data scientists and establish best practices for model development, documentation, and code quality.

REQUIREMENTS

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field; Master's or PhD preferred.
  • 5+ years | Python + SQL | production ML | predictive modeling | model deployment | MLOps | cloud | measurable business impact
  • Proven ability to deliver measurable business impact through data science and machine learning.
  • Strong communication, analytical, and business problem-solving skills.
  • Expert Python and SQL skills.

TECHNICAL EXPERIENCE

  • Machine Learning: XGBoost, LightGBM, Random Forest, Neural Networks, Deep Learning
  • Statistics: Regression, Bayesian methods, hypothesis testing, experimental design, causal inference, time series
  • Data & Cloud: Snowflake, dbt, Spark, Airflow, GCP preferred; AWS or Azure considered
  • MLOps: MLflow, Kubeflow, Vertex AI, feature stores, CI/CD, model monitoring
  • AI/LLMs: OpenAI, Gemini, Claude, LangChain, LangGraph, RAG, embeddings, vector databases
  • Experience with data quality and observability tools such as Great Expectations or Monte Carlo is a plus.

*You do not need experience with every technology listed above. Strong production machine learning experience is the priority.

Preferred Experience

  • Large-scale customer or behavioral data
  • Marketing analytics, personalization, or customer intelligence
  • SaaS, automotive, retail, advertising, or marketing technology
  • Real-time inference or streaming data
  • Production Generative AI applications

COMPENSATION & BENEFITS
The expected starting salary is $140,000 annually, based on experience, skills, and qualifications. Benefits include medical, dental, vision, 401(k) matching, unlimited paid leave, wellness programs, and more.

NEXT STEPS
If you meet the requirements, and are interested in applying for this role, please complete the online employment application and be sure to upload a current resume and current contact information.

About Team Velocity
Team Velocity is a full-service marketing and technology company serving automotive manufacturers and dealerships nationwide. Our proprietary Apollo® technology platform uses data, predictive analytics, and AI to predict consumer behavior, personalize marketing, and help dealerships increase sales and service revenue.

Join us in applying data science, machine learning, and AI to real-world business problems at scale.