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Data Science Machine Learning Jobs in Boston, MA

Roberts Recruiting

Senior Data Scientist

Boston, MA · On-site

The Data Scientist will analyze and model expected crop yield under various scenarios. Using large ... applied computer science, machine learning, mathematics, statistics, quantitative agronomy ...

Inmar

Director, Data Science (MarTech)

Boston, MA

The Director, Data (MarTech) is responsible for applying data exploration and visualization, machine learning and artificial intelligence, and other data science techniques to explore, create, and ...

Lutron Electronics

Principal Data Scientist

Boston, MA · On-site

... science, machine learning, and AI with sustained, enterprise-level impact • Proven leadership track record leading teams and projects, coordinating across functions, and driving execution from ...

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Manulife

Director, AI

Boston, MA

Provide technical leadership across traditional data science, predictive modeling, machine learning, GenAI, LLMs, and production AI engineering. * Guide the team in building modular, scalable ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$124K - $149K/yr

Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery ... What We're Looking For (Must-Haves): * BS or MS in Computer Science, Machine Learning, or a related ...

Ipsen

Senior Data Scientist

Cambridge, MA

Stay abreast of advancements in data science, machine learning, and healthcare technologies; * Educate and raise awareness around Data Science and its potential in supporting Ipsen's internal teams ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

MS/PhD in Computer Science, Machine Learning, or related field. * Experience with agentic systems ... Experience with ML-based data mining, active learning, or contrastive learning. * Knowledge of ...

Motional

Machine Learning Engineer, Data Mining

Boston, MA · On-site +1

$144K - $192K/yr

MS/PhD in Computer Science, Machine Learning, or related field. * Experience with agentic systems ... Experience with ML-based data mining, active learning, or contrastive learning. * Knowledge of ...

Lutron Electronics

Data Scientist

Boston, MA · On-site

... science, analytics, machine learning, and AI to improve customer experiences, products, and business operations, collaborating with various teams to turn data into actionable insights.

CarGurus

Manager, Data Science

Boston, MA

Role overview The Manager, Data Science will lead an Inventory & Dealer Data Science team focused ... The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production.

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Showing results 1-20

All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Boston, MA

Data Science Machine Learning information

See Boston, MA salary details

$40.7K

$133.3K

$213.5K

How much do data science machine learning jobs pay per year?

As of Jun 29, 2026, the average yearly pay for data science machine learning in Boston, MA is $133,336.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $147,700.00 per year, depending on experience, location, and employer.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Boston, MA as of June 2026, with employment types broken down into 51% Full Time, 38% Part Time, and 11% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $133,336 per year, or $64.1 per hour.
Senior Data Scientist

Senior Data Scientist

Roberts Recruiting

Boston, MA • On-site

Other

Posted 11 days ago

Job description

We are working to harness nature to sustainably feed the planet. We have discovered a transformational opportunity to improve global crop yield and reduce the use of agricultural chemicals and fertilizers by utilizing the core microbiome inside plants to confer material yield and crop protection benefits across a variety of crops, geographies and stresses. The challenge and opportunity for usis to gain critical first mover advantages by scaling quickly and effectively (~$1B 2020 revenue.)
The Data Scientist will enable optimal data-driven decision making and dramatically improving farmers' outcomes.
The Data Scientist will analyze and model expected crop yield under various scenarios. Using large-scale cloud and machine learning algorithms, the Data Scientist will construct data preprocessing pipelines, predictive models, optimization algorithms, and decision support systems.
Key Outcomes:
  • Collect and preprocess IoT-scale data from on-farm and remote monitoring equipment including: yield, farm treatments, irrigation, soils, satellite and drone imagery
  • Analyze agricultural data to understand the impact of agronomic decisions on crop health and crop yield.
  • Use state-of-the-art machine learning tools to understand crop yields as a function of microbiome, crop, planting conditions, soil, weather, farm treatments, and farm conditions
  • Produce compelling and clear visualizations of agronomic and statistical findings.
  • Partner closely with Software Development to turn Data Science products into best-in-class end-user applications.
Key Competencies:
  • Masters Degree + 2-5 years industry experience in a relevant field such as applied computer science, machine learning, mathematics, statistics, quantitative agronomy, environmental science, applied physics or engineering.
  • Outstanding communication skills - Outgoing and enthusiastically enjoys explaining statistical analyses and methods to customers and decision makers throughout the organization.
  • Experience in and/or eagerness to learn about agriculture, agronomy, GIS, spatial statistics, plant biology, microbiology, agricultural economics, and the plant microbiome.
  • Absolutely high fluency with R and/or Python and Bayesian MCMC tools such as JAGS or STAN, as well as conventional statistical analyses, machine learning, and GIS. Strong ability to produce lucid graphical plots, maps, and highly interactive visualizations. Experience working in various computational environments, such Linux, ec2, S3, Spark, and H2O.
  • Team player - someone who thrives working in cross functional teams throughout the organization.
  • A curious, scientific mind dedicated to teasing apart new results through careful, quantitative analysis joined with sound scientific practice.
  • Extremely strong customer focus, seeing growers and agronomists as our key end users, and grower success as our ultimate goal.
  • Easily adapts to new types of problems and questions. Flexible to different types of analysis and different stakeholders (e.g. lab science, field trials, commercial team).
  • Experience working in industry and a dedication to Data Science performed in an industrial setting.
  • Deep understanding of modern Bayesian statistics, ensemble classifiers, regression algorithms, as well as off the shelf machine learning techniques. Experience with model validation and evaluation, and data imputation.
  • Familiarity with SQL as well as NoSQL columnar databases. Experience with MILP, combinatorial optimization, metaheuristics, and other types of optimization are an added advantage.
  • Experience with agronomy or ecology work including modeling, forecasting, decision support, and optimization

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