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Data Science Jobs (NOW HIRING)

Fidelity Investments

Director, Data Science

Boston, MA · On-site

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 ...

PROPRIUS

Director Data Science

San Francisco, CA

Director Data Science Location: San Francisco, CA Sponsorship: Yes Relocation: Yes Industry: Data Science A Fortune 500 is looking for a proven Data Science leader to create a new Data Science team.

Optimize Partners

Director, Data Science

New York, NY · Hybrid

As a manager of a world-class data science team within a fast-evolving financial landscape, the individual will have the exciting opportunityto direct a team of data scientists on a variety of ...

JCTM, LLC

Data Science SME

Quantico, VA · On-site

$119K - $133K/yr

What if you could apply your data science expertise to help the Marine Corps Warfighting Lab identify opportunities to improve situational awareness, decision-making, and operational effectiveness ...

Elsevier

Manager Data Science

Philadelphia, PA

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians ...

RELX

Manager Data Science

Philadelphia, PA

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians ...

Elsevier

Manager Data Science

Philadelphia, PA · On-site

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians ...

RELX Group plc

Manager Data Science

Philadelphia, PA · On-site

$115K - $192K/yr

At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians ...

Numerator

Director, Data Science

Chicago, IL · On-site

As Director of Data Science , you will lead the team responsible for Numerator's consumer panel - the quality, currency, and representativeness of the signal at the heart of our data. Your team owns ...

Novartis

Director, Data Science

East Hanover, NJ · On-site

They are seeking a Director of Data Science to lead the development and deployment of advanced AI solutions that enhance operational efficiency and drive strategic growth across the organization.

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DATA Science information

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How much do data science jobs pay per year?

As of Jul 23, 2026, the average yearly pay for data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

Is data science a good career?

Data science is a growing field with high demand for professionals skilled in statistics, programming, and data analysis tools like Python and R. It offers competitive salaries, diverse industry applications, and opportunities for advancement, making it a strong career choice for those with relevant skills and education.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What jobs can a Data Scientist do?

A Data Scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What work do you do as a Data Scientist?

A Data Scientist analyzes large datasets to extract insights, build predictive models, and inform business decisions. They use programming languages like Python or R, and tools such as SQL and machine learning frameworks, often working in collaborative environments with data engineers and analysts.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
More about DATA Science jobs
What cities are hiring for Data Science jobs? Cities with the most Data Science job openings:
What are the most commonly searched types of Data Science jobs? The most popular types of Data Science jobs are:
What states have the most Data Science jobs? States with the most job openings for Data Science jobs include:
What job categories do people searching Data Science jobs look for? The top searched job categories for Data Science jobs are:
DATA Science jobs near you
Infographic showing various Data Science 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 $122,738 per year, or $59 per hour.
Director, Data Science

Director, Data Science

Fidelity Investments

Boston, MA • On-site

Full-time

Medical, Retirement, PTO

Posted 20 days ago

Fidelity Investments rating

8.8

Company rating: 8.8 out of 10

Based on 268 frontline employees who took The Breakroom Quiz

9th 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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