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Economics Data Science Jobs (NOW HIRING)

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Financial Data Science Advisor

Reston, VA · On-site

The Finance - Data Science - Advisor role will offer you the flexibility to make each day your own ... Use advanced mathematical, analytical, or econometric tools to create algorithms and analyses that ...

Yum Brands

Sr. Data Scientist

Plano, TX · On-site

$117K - $132K/yr

Bachelor's degree from an accredited institution in Statistics, Mathematics, Data Science, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent relevant experience.

Pizza Hut

Sr. Data Scientist

Plano, TX · On-site

$117K - $132K/yr

Bachelor's degree from an accredited institution in Statistics, Mathematics, Data Science, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent relevant experience.

Fanniemae

Audit Data Science Advisor

Reston, VA · On-site

Master's degree in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field. * Advanced proficiency in Python and core data science and machine learning techniques.

Fanniemae

Audit Data Science Advisor

Washington, DC · On-site

Master's degree in Data Science, Economics, Mathematics, Statistics, Computer Science, or a related field. * Advanced proficiency in Python and core data science and machine learning techniques.

State of Iowa

Data Analyst 1

Des Moines, IA · On-site

$63K - $98K/yr

Four years of full-time work experience in business/data/statistical analytics, economic research, or data science. A total of four years of education and/or full-time experience (as described in ...

Totalwine

Bachelor's Degree Data Science, Computer Science, Statistics, Mathematics, Economics, or related field required or equivalent years of experience * Master's Degree Data Science, Computer Science ...

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How much do economics data science jobs pay per year?

As of Aug 21, 2026, the average yearly pay for economics data science in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

What is an economics data science?

An Economics Data Science job combines economic theory, statistical analysis, and machine learning to interpret complex data and inform decision-making. Professionals in this field work with large datasets to analyze market trends, forecast economic conditions, and optimize business strategies. They commonly use programming languages like Python or R and tools such as SQL and econometric models. This role is valuable in industries like finance, government, and tech, where data-driven economic insights are crucial.

What are common projects or responsibilities for professionals in economics data science roles?

Economics Data Science professionals often work on projects that involve economic modeling, market analysis, and forecasting trends using large datasets. Typical responsibilities include gathering and cleaning economic data, developing predictive models, conducting statistical analyses, and translating results into actionable recommendations for business strategy or policy decisions. You may collaborate closely with economists, business analysts, and decision-makers to inform company direction or solve complex real-world problems. This role often involves a mix of independent data exploration and teamwork, offering opportunities to drive impactful results and shape organizational strategy.

What are the key skills and qualifications needed to thrive in economics data science, and why are they important?

To thrive in Economics Data Science, you need a strong background in economics, statistics, and programming (especially Python or R), often backed by a degree in economics, data science, or a related field. Proficiency with data analysis tools such as SQL, machine learning frameworks, and visualization software like Tableau is commonly required, and certifications in data science or analytics are advantageous. Excellent problem-solving abilities, communication skills, and a collaborative mindset help distinguish top performers in this position. These skills are crucial for effectively analyzing economic data, generating actionable insights, and conveying complex findings to both technical and non-technical stakeholders.

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What are the most commonly searched types of Economics Data Science jobs?

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Economics Data Science jobs near you
Infographic showing various Economics Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.

Senior Data Scientist - Media Data Science & Analytics

Microsoft

Redmond, WA • On-site

Full-time

Posted 17 days ago

Microsoft rating

8.5

Company rating: 8.5 out of 10

Based on 132 frontline employees who took The Breakroom Quiz

79th of 245 rated software companies

Job description

Overview
We're building a Frontier Marketing organization where the Media Data Science & Analytics team leads the way in transforming how Microsoft measures, analyzes, and optimizes media investments. Our team blends advanced analytics, experimentation, and AI-powered insights to drive smarter decision-making and measurable business outcomes across paid media and owned digital properties.
We operate with agility, prioritize outcomes over activity, and embrace rapid learning loops to unlock deeper audience understanding, maximize campaign impact, and accelerate innovation in media strategy.
To support this transformation, we are seeking a Senior Data Scientist to help us measure the incremental impact of advertising spend and use that to help our media planning partners optimize media campaigns.
Marketing data science is inherently challenging: data is often observational, incomplete, biased, or limited in scale, and outcomes unfold over time across complex systems. The successful candidate will be someone who can apply rigorous causal methods, exercise sound statistical judgment, and translate uncertainty into actionable insights that inform high-stakes investment decisions.
Responsibilities
Causal Measurement & Business Impact
  • Design and apply causal inference approaches (e.g., quasi-experimental methods, incrementality testing, observational analysis) to estimate the true impact of media investments in settings where randomized experiments may be limited or infeasible.
  • Evaluate the effectiveness of marketing strategies while explicitly accounting for data limitations, confounding, selection bias, and uncertainty.
  • Translate complex causal findings into clear, decision-oriented narratives for senior marketing and business stakeholders.

Modeling, Statistics & Analysis
  • Apply advanced statistical techniques and machine learning where appropriate, with a bias toward interpretability and causal validity over purely predictive performance.
  • Balance methodological rigor with pragmatism, selecting approaches that are fit for purpose given the data and business context.
  • Write high-quality analytical code (Python, SQL) to support reproducible research, exploratory analysis, and ongoing measurement efforts.
  • Identify opportunities to improve measurement approaches, challenge existing assumptions, and introduce best practices grounded in both academic research and industry experience.

Data Understanding & Stewardship
  • Prepare, validate, and analyze complex marketing datasets, identifying data quality issues, structural changes, and limitations that materially affect inference.
  • Communicate data risks, constraints, and implications proactively to senior partners, ensuring conclusions are appropriately scoped and caveated.
  • Uphold high standards for data ethics, privacy, and responsible use, with careful attention to how data is collected, modeled, and interpreted.

What Success Looks Like
  • Media investment decisions are better informed by clear, credible causal insights rather than surface-level correlations.
  • Stakeholders understand not only what the data suggests, but how confident we are and why.
  • Analytical recommendations appropriately reflect data constraints and uncertainty, earning trust through transparency and rigor.
  • The team consistently applies causal thinking to difficult, ambiguous marketing problems, even when the data is imperfect.

Qualifications
Required/Minimum Qualifications
  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.

Preferred Qualifications
  • Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 6+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 8+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
  • 5+ years' experience building ML models.
  • 5+ years' experience writing SQL to analyze data.
  • 5+ years' experience writing code in Python.
  • 3+ years' communicating complex technical concepts to non-technical partner teams.
  • 1+ years' experience performing causal inference
  • 1+ years' experience with media / marketing data science

Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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About Microsoft

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Our infrastructure is comprised of a large global portfolio of more than 100 datacenters and 1 million servers. Our foundation is built upon and managed by a team of subject matter experts working to support services for more than 1 billion customers and 20 million businesses in over 90 countries worldwide. With environmental sustainability and optimization at the forefront of our datacenter design and operations, we continue to grow and evolve as we meet the ever-changing business demands that hold Microsoft as a world-class cloud provider.

Industry

Computer and computer peripheral equipment and software wholesalers

Company size

10,000+ Employees

Headquarters location

Redmond, WA, US

Year founded

1975

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