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Data Science Research Jobs in Boston, MA (NOW HIRING)

Fidelity Investments

Director, Data Science

Boston, MA · On-site

$126K/yr

Oversees end-to-end process to push code from research to production. Delivers results with clear ... a data science team within the Quantitative Research and Investment Technology division in the ...

Folia Health

Research Scientist

Boston, MA

$98K - $125K/yr

Partner with Data Science to advance Folia's Home Reported Outcomes methodology across the design and execution of research programs, ensuring data collection faithfully captures the patient ...

CarGurus

Manager, Data Science

Boston, MA · On-site

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.

Formation Bio

Director, Data Science

Boston, MA · On-site

$235K - $307K/yr

Formation Bio partners, acquires, or in-licenses drugs from pharma companies, research ... About the Position As the Director of Data Science at Formation Bio, you will be at the forefront ...

Newton Research

Data Scientist

Boston, MA

$160K - $180K/yr

Data Scientist Company Description Newton Research is a fast-growing software start-up founded by repeat entrepreneurs and well-funded by blue chip venture capital firms. We are building the next ...

XPO

Senior Scientist, Data Science - Hybrid

Boston, MA

$106K - $132K/yr

Bachelor's degree or equivalent related work or military experience * 2 years of experience in data science, operations research, or data analysis with a focus on AI-driven analytics, statistical ...

CarGurus

Manager, Data Science

Boston, MA · On-site

Sitting within the broader Data Science organization, this team is responsible for modeling ... The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production.

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All JobsData Science Research JobsData Science Research Jobs in Boston, MA

Data Science Research information

See Boston, MA salary details

$40.7K

$133.3K

$213.5K

How much do data science research jobs pay per year?

As of Jul 29, 2026, the average yearly pay for data science research in Boston, MA is $133,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,000.00 and $147,800.00 per year, depending on experience, location, and employer.

Is 40 too late for data science?

Data science research roles do not have strict age limits, and many professionals start or transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications, regardless of age.

How does a Data Science Researcher typically collaborate with other departments within an organization?

Data Science Researchers frequently work cross-functionally, collaborating with teams such as engineering, product management, and business analytics. They often translate complex research findings into actionable insights, guiding product development or business strategies. Regular meetings, joint project planning, and code reviews are common, ensuring that research outcomes align with organizational goals. Effective communication and teamwork are key to integrating advanced data solutions into real-world applications.

What do data science researchers do?

Data science researchers analyze large datasets to identify patterns, develop models, and generate insights that inform decision-making. They often use programming languages like Python or R, statistical methods, and machine learning techniques, working in research environments or industry settings to advance knowledge or solve complex problems.

What is the highest paid job in data science?

The highest paid roles in data science are often senior positions such as Lead Data Scientist, Chief Data Officer, or Data Science Director, with salaries exceeding $150,000 annually. These roles typically require extensive experience, advanced skills in machine learning and big data tools, and often involve strategic decision-making responsibilities.

What is the difference between Data Science Research vs Data Analyst?

AspectData Science ResearchData Analyst
CredentialsTypically requires advanced degrees (Master's or PhD) in Data Science, Statistics, or related fieldsOften requires a Bachelor's or Master's degree in Data Analysis, Statistics, or related areas
Work EnvironmentResearch labs, academic institutions, or R&D departments within companiesBusiness environments, corporate offices, or consulting firms
Employer & Industry UsageUniversities, research institutions, tech companies focusing on innovationRetail, finance, healthcare, and other industries focusing on data-driven decision making

Data Science Research focuses on developing new algorithms, models, and theories, often in academic or R&D settings. In contrast, Data Analysts primarily interpret existing data to generate reports and insights for business decisions. Both roles require strong analytical skills but differ in scope, goals, and work environment.

Is a Data Scientist in high demand?

Data Scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to remain strong in the coming years.

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

To thrive as a Data Science Researcher, you need strong analytical skills, expertise in statistics and machine learning, and an advanced degree in a quantitative field such as computer science, mathematics, or engineering. Proficiency with programming languages like Python or R, data visualization tools, and experience using platforms such as TensorFlow or PyTorch is typically required. Curiosity, creativity, and clear communication are essential soft skills for designing research questions, interpreting results, and sharing findings with diverse audiences. These skills and qualities are crucial for driving innovative solutions and impactful insights in data-driven environments.

What is data science research?

Data science research involves using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Researchers in this field work on developing new data analysis techniques, machine learning models, and data-driven solutions to solve complex problems. The work often includes designing experiments, analyzing large datasets, and publishing findings to advance the understanding of data science methodologies.
What are popular job titles related to Data Science Research jobs in Boston, MA? For Data Science Research jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Data Science Research jobs in Boston, MA look for? The top searched job categories for Data Science Research jobs in Boston, MA are:
Data Science Research jobs near you
Infographic showing various Data Science Research job openings in Boston, MA as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, 1% Temporary, and 4% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $133,343 per year, or $64.1 per hour.
Director, Data Science

Director, Data Science

Fidelity Investments

Boston, MA • On-site

Full-time

Medical, Retirement, PTO

Posted 26 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 270 frontline employees who took The Breakroom Quiz

15th of 150 rated financial services

Job description


Principal Data Scientist - Quantitative Decision Science & Advanced Analytics
Note: Fidelity will not provide immigration sponsorship for this position.
Are you interested in operating as a senior scientific leader-owning truth, rigor, and decision quality for complex business problems? Fidelity Institutional's AI Center of Excellence (AI CoE) is seeking a Principal Data Scientist to serve as a highly tenured individual contributor and domain authority in data science, quantitative modeling, and advanced analytics.
This role is intentionally Data Science-first, with emphasis on hypothesis-driven analysis, statistical rigor, causal reasoning, and decision science. The Principal Data Scientist is accountable for what the model means, whether it is correct, and whether it should be trusted-not for building or operating production systems.
The Team
The Data Science function within the Fidelity Institutional AI CoE operates as the authority on measurement, experimentation, and quantitative decision-making. The team comprises senior data scientists, statisticians, and quantitative researchers who partner closely with platform, product, BI, and business teams, while maintaining clear ownership of scientific rigor, evaluation frameworks, and analytical truth.
As a Principal Data Scientist, you will operate as a scientific owner and mentor, influencing methodology, standards, and strategic direction across multiple initiatives.
Key Responsibilities
Advanced Data Science & Quantitative Modeling
  • Lead hypothesis-driven analyses to answer high-impact strategic and business questions

  • Design, develop, and evaluate statistical, econometric, and machine learning models where appropriate

  • Ensure models are theoretically sound, empirically validated, interpretable, and fit-for-purpose

  • Review and challenge modeling approaches for bias, stability, assumptions, and misuse

Measurement, Evaluation & Decision Science
  • Define how success should be measured for complex analytics and AI-enabled initiatives

  • Design robust evaluation frameworks including offline validation, back-testing, and live measurement

  • Ensure stakeholders can distinguish correlation from causation in analytical results

  • Elevate analytics from prediction accuracy to decision quality and business impact

Experimentation & Causal Inference
  • Design and review experiments including A/B tests, quasi-experiments, and observational studies

  • Apply causal inference techniques (e.g., uplift modeling, DiD, matched controls) to assess incrementality

  • Guide best practices for power analysis, inference, and result interpretation

  • Serve as a subject-matter expert on "What worked, why, and by how much?"

Advanced Analytics Domains
  • Segmentation & Clustering: Design statistically grounded, interpretable segmentations with clear hypotheses and stability checks

  • Propensity, Likelihood & Uplift Modeling: Develop probabilistic and causal models to inform prioritization and intervention strategies

  • Recommendation & Prioritization Analytics: Guide recommendation logic rooted in statistics, behavioral science, and optimization-not black-box ML

  • Behavioral & Journey Analytics: Analyze longitudinal behavior patterns to identify drivers, frictions, and causal levers

  • Forecasting & Planning Analytics: Apply time-series and probabilistic forecasting with uncertainty and scenario analysis

  • Large Language Models & Generative AI: Design, evaluate, and implement LLM-based solutions - including RAG pipelines, classification, and extraction tasks - with rigorous benchmarking, calibration analysis, hallucination measurement, and bias auditing to ensure outputs are explainable.

Scientific Leadership & Governance (Non-Managerial)
  • Act as a senior reviewer and methodological authority across data science initiatives

  • Set informal standards for rigor, documentation, and reproducibility

  • Mentor senior and mid-level data scientists through technical guidance and peer review

Business Partnership & Influence
  • Translate complex quantitative results into clear, decision-oriented narratives for senior stakeholders

  • Challenge assumptions and narratives not supported by evidence

  • Influence strategy by grounding discussions in data, causality, and expected impact

Expertise and Skills You Bring
Education & Experience
  • Master's or PhD in Statistics, Economics, Mathematics, Operations Research, Computer Science, or related quantitative discipline

  • 10-14+ years of experience in data science, quantitative research, or advanced analytics

  • Proven track record of owning complex analytical problems end-to-end (from question formulation to decision impact)

Core Data Science & Scientific Expertise
  • Deep expertise in statistics, probability, and experimental design

  • Strong command of causal inference and incrementality measurement

  • Solid grounding in forecasting, optimization, and decision science

  • Demonstrated ability to assess modeling correctness, assumptions, and limitations

Technical Foundation
  • Advanced proficiency in Python for analysis and modeling (NumPy, Pandas, SciPy, Statsmodels, Scikit-learn)

  • Strong SQL skills and experience working with large analytical datasets (e.g., Snowflake)

  • Hands-on proficiency with large language models and generative AI, including prompt design, retrieval-augmented generation, structured outputs, and agentic workflows, with demonstrated rigor in designing evaluations, defining task-specific metrics, and applying statistical testing to assess reliability, calibration, hallucination risk, and incremental value over non-generative approaches. Equally proficient in hands-on code development as well as the effective use of AI-powered coding assistants, applying both to accelerate analysis while maintaining correctness, reproducibility, and scientific rigor.

Ways of Working
  • Thinks like a scientist: hypothesis-first, evidence-driven, and principled

  • High bar for rigor, interpretability, and defensibility of results

  • Comfortable challenging senior stakeholders using data and logic

  • Values clarity, elegance, and correctness over technical novelty

  • Operates as a trusted expert rather than a delivery engineer

How This Role Is Distinct
  • Senior Individual Contributor: Tenured individual-contributor role with broad organizational influence

  • Data Science-First: Focused on analytics, statistics, causality, and decision science

  • Strategic Impact: Owns critical analytical questions that shape business decisions and investments

Fidelity's Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
The base salary range for this position is $126,000-255,000 USD per year.
Placement in the range will vary based on job responsibilities and scope, geographic location, candidate's relevant experience, and other factors.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.
We offer a wide range of benefits to meet your evolving needs and help you live your best life at work and at home. These benefits include comprehensive health care coverage and emotional well-being support, market-leading retirement, generous paid time off and parental leave, charitable giving employee match program, and educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career. Note, the application window closes when the position is filled or unposted.
Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.
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