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Ml Data Associate Jobs in Boston, MA (NOW HIRING)

Headquartered in Needham, Massachusetts with more than 4,100 associates, the company's products are ... Partnerships Collaborate with Director, ML and AI, Security, Legal, Procurement, and Global Data ...

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science ... AWS Certified Cloud Practitioner, AWS Certified Solutions Architect (Associate), AWS Certified AI ...

Lead Forward Deployed Engineer - AWS

Boston, MA · On-site

$111K - $146K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science ... AWS Certified Cloud Practitioner, AWS Certified Solutions Architect (Associate), AWS Certified AI ...

Senior Forward Deployed Engineer- AWS

Boston, MA · On-site

$113K - $155K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science ... AWS Certified Cloud Practitioner, AWS Certified Solutions Architect (Associate), AWS Certified AI ...

AI Innovation & Responsible Generative AI Design and develop AI solutions using Python, AI/ML and ... Associate benefits are designed to encourage personal wellness and smart healthcare decisions for ...

AI Innovation & Responsible Generative AI Design and develop AI solutions using Python, AI/ML and ... Associate benefits are designed to encourage personal wellness and smart healthcare decisions for ...

AI Innovation & Responsible Generative AI Design and develop AI solutions using Python, AI/ML and ... Associate benefits are designed to encourage personal wellness and smart healthcare decisions for ...

Showing results 41-60

Ml Data Associate information

See Boston, MA salary details

$62.5K

$73.9K

$140.1K

How much do ml data associate jobs pay per year?

As of Sep 2, 2026, the average yearly pay for ml data associate in Boston, MA is $73,917.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $64,600.00 per year, depending on experience, location, and employer.

What is an ml data associate?

ML Data Associates are professionals who support machine learning projects by preparing, labeling, and validating data used to train and evaluate algorithms. They often work with large datasets, ensuring data quality and accuracy, and may use specialized tools to annotate images, text, or audio. Their work is essential for enabling machine learning models to learn from high-quality, well-structured data, and they often collaborate with data scientists and engineers to optimize data pipelines.

What are the key skills and qualifications needed to thrive as an ml data associate?

To thrive as an ML Data Associate, you need strong analytical skills, attention to detail, and a solid understanding of data annotation or labeling, often supported by a degree in a technical field. Familiarity with data labeling tools, basic programming (such as Python), and experience working with machine learning platforms are typically required. Excellent communication, problem-solving abilities, and the capacity to work efficiently in teams are important soft skills. These skills ensure high-quality, accurately labeled datasets that are essential for training effective machine learning models.

What are some common challenges faced by ml data associates when labeling complex datasets, and how can they be effectively addressed?

ML Data Associates often encounter challenges with ambiguous data, inconsistent labeling guidelines, or rapidly evolving project requirements. To address these, it's important to maintain open communication with data scientists and project leads, ask clarifying questions, and participate in regular calibration sessions to ensure consistency. Utilizing annotation tools efficiently and staying up-to-date with best practices can also help manage complexity and improve label quality. Collaboration and feedback within the team are key to overcoming these challenges and ensuring high-quality datasets.

What is the difference between Ml Data Associate vs Data Analyst?

AspectML Data AssociateData Analyst
Required CredentialsTypically a degree in computer science, data science, or related field; familiarity with machine learning conceptsUsually a degree in statistics, mathematics, or business analytics; strong Excel and data visualization skills
Work EnvironmentTech companies, AI startups, or organizations focusing on machine learning projectsBusiness, finance, marketing, and consulting firms analyzing data for insights
Employer & Industry UsageUsed in industries developing AI models, machine learning pipelines, and data infrastructureCommon across industries for reporting, trend analysis, and strategic decision-making

While both roles involve working with data, ML Data Associates focus on preparing and managing data specifically for machine learning models, whereas Data Analysts interpret data to generate business insights. The roles overlap in data handling skills but differ in their end goals and technical focus.

How do I become an ML Data Associate?

To become an ML Data Associate, candidates typically need a high school diploma or equivalent, along with strong attention to detail and organizational skills. Familiarity with data management tools, basic understanding of machine learning concepts, and experience with data annotation or labeling are often required. Some roles may also require knowledge of programming languages like Python or experience with data annotation platforms.

What skills do you need for a machine learning data associate job?

A machine learning data associate needs strong analytical skills, attention to detail, and proficiency in data management tools like Excel, SQL, or Python. Knowledge of data cleaning, labeling, and basic understanding of machine learning concepts are also important for the role.

What are popular job titles related to Ml Data Associate jobs in Boston, MA?

For Ml Data Associate jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Ml Data Associate jobs in Boston, MA look for?

The top searched job categories for Ml Data Associate jobs in Boston, MA are:

What cities near Boston, MA are hiring for Ml Data Associate jobs?

Cities near Boston, MA with the most Ml Data Associate job openings:

Infographic showing various Ml Data Associate job openings in Boston, MA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $73,917 per year, or $35.5 per hour.

Team Leader in Data Science, Disease Area X

Novartis Pharmaceuticals Corporation

Cambridge, MA

Full-time

Medical, Life, Retirement, PTO

Re-posted 11 days ago


Job description

Band

Level 5


Job Description Summary

The Team Leader in Data Science, Disease Area X at Novartis will lead and contribute to high-impact data science programs that transform complex biological, translational, and multi-omics data into decision-driving insights for drug discovery. This role will combine scientific leadership, hands-on computational expertise, and people leadership to advance target identification, biomarker discovery, mechanism-of-action understanding, and portfolio decisions.
The successful candidate will lead a multidisciplinary team of data scientists and partner closely with biology, translational research, data sciences, IT, and discovery platform teams. They will help define and operationalize AI/ML strategy for discovery applications, including generative and agentic AI. This leader will also contribute significantly to data generation, curation, and engineering strategies that enable scalable use of proprietary and public datasets. The role reports to the Head of Data Science, Disease Area X.


Job Description

Internal Job Title: Senior Principal Scientist or Associate Director

Position Location: Cambridge, MA Hybrid

Key responsibilities:

  • Lead data science strategy and executionfor hypothesis-driven discovery programs, including study design, analysis of experiments, and interpretation of complex biological datasets.

  • Drive multi-omics analyticsacross genomics, transcriptomics, proteomics, single-cell, spatial, imaging, clinical, and other relevant data modalities to support target and biomarker portfolios.

  • Translate scientific questions into computational strategies, selecting fit-for-purpose statistical, machine learning, AI, and bioinformatics approaches.

  • Operationalize responsible use of generative and/or agentic AI tools in drug discovery workflows, ensuring scientific rigor, data governance, and appropriate human oversight.

  • Contribute hands-on technical workin scientific software development, data engineering, workflow automation, reproducible analysis, and scalable analytical pipelines.

  • Partner cross-functionallywith wet-lab scientists, translational researchers, platform teams, and senior stakeholders to shape experimental design and accelerate decision-making.

  • Prioritize resources and capabilitiesacross multiple projects, adapting to evolving portfolio needs and balancing strategic impact with delivery timelines.

  • Lead, coach, and develop direct reports, creating a collaborative, inclusive, scientifically rigorous, and high-performing team environment.

  • Communicate scientific findings and recommendationsclearly through internal presentations, governance discussions, publications, posters, and external scientific forums.

  • Promote FAIR data practices, reproducible research, high-quality documentation, project tracking, and scalable analytical standards across the team.

Essential Requirements:

  • Advanced degree (PhD preferred) in Data Science, Computational Biology, Bioinformatics, Computational Science, Molecular Biology, Genetics, Biochemistry, Engineering, or a related quantitative or life sciences discipline.

  • 6+ years of relevant experience applying computational biology, bioinformatics, AI/ML, statistics, or data science to drug discovery, translational research, biotechnology, pharmaceutical R&D, technology, or academic research.

  • Experience leading or managing internal data scientists, computational biologists, bioinformaticians, or machine learning scientists in a matrix management environment as well as external collaborators

  • Demonstrated ability to lead complex, hypothesis-driven scientific analyses using biological, multi-omics, or translational datasets, including RNA-seq, single-cell RNA-seq, proteomics, genomics, spatial biology, and/or imaging.

  • Strong practical experience with scientific software development, reproducible analysis, workflow orchestration and collaborative development practices; experience in Python and/or R, with familiarity in tools such as GitHub, HuggingFace, workflow managers, Jupyter notebooks, containers

  • Deep experience with cloud-based or enterprise-scale compute platforms, high-performance computing

  • Significant experience influencing and collaborating across diverse scientific teams, including wet-lab biology, translational research, engineering, and computational functions.

  • Familiarity with modern AI/ML methods and their application to biological or biomedical data (i.e. generative, agentic AI)

  • Experience acquiring, curating, and engineering proprietary and public datasets while maintaining appropriate data governance, privacy, and security standards.

  • Demonstrated ability to shape scientific strategy cross-functionally, influence senior stakeholders, and translate analytical results into portfolio-relevant decisions.

  • Track record of scientific impact through publications, conference presentations, internal decision support, or portfolio contributions.

  • Strong communication, interpersonal, ethical judgment, resilience, and self-awareness skills.

Compensation & Benefits:

The salary for this position is expected to range between $160,300 and $297,700 USD annually for Senior Principal Scientist, Data Science, and $176,400 and $327,600 USD annually for Associate Director, Data Science. The final salary offered is determined based on factors like, but not limited to, relevant skills andexperience, and upon joining Novartis will be reviewed periodically. Novartis may change the publishedsalary range based on company and market factors.


Your compensation will include a performance-based cash incentive and, depending on the level of therole, eligibility to be considered for annual equity awards.


US-based eligible employees will receive a comprehensive benefits package that includes health, life anddisability benefits, a 401(k) with company contribution and match, and a variety of other benefits. Inaddition, employees are eligible for a generous time off package including vacation, personal days,holidays and other leaves.


To learn more about the culture, rewards and benefits we offer our people click here.


EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.


Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
https://www.novartis.com/careers/careers-research/notice-all-applicants-us-job-openings


Salary Range

$160,300.00 - $297,700.00


Skills Desired

Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis