1

Data Science Phd Jobs in Virginia (NOW HIRING)

Senior Associate, Data Science Data is at the center of everything we do. As a startup, we ... Master's Degree in "STEM" field (Science, Technology, Engineering, or Mathematics), or PhD in "STEM ...

MSc/PhD in statistics, applied mathematics, computer science, operational research, economics or ... conceptual thinking, data visualisation expertise, and problem-solving ability. Effective ...

MSc/PhD in statistics, applied mathematics, computer science, operational research, economics or ... conceptual thinking, data visualisation expertise, and problem-solving ability. Effective ...

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field (PhD preferred for some roles) * 5+ years of experience in data science or a related field

Showing results 21-40

Data Science Phd information

What is a data science PhD?

A Data Science PhD is a doctoral-level degree focused on advanced research in data science, which combines elements of statistics, computer science, and domain expertise. Students in a Data Science PhD program typically work on developing new methods for analyzing large datasets, creating machine learning algorithms, and addressing complex problems in areas such as artificial intelligence, data mining, and predictive analytics. Graduates are prepared for careers in academia, research, and industry, where they can lead data-driven projects and contribute to advancements in the field.

What are the key skills and qualifications needed to thrive as a data science PhD?

To thrive as a Data Science PhD, you need advanced expertise in statistics, machine learning, data analysis, and a doctoral degree in a quantitative field. Proficiency in programming languages like Python or R, experience with big data frameworks (e.g., Spark, Hadoop), and familiarity with data visualization tools are typically required. Critical thinking, problem-solving, and strong communication skills help you translate complex data insights for diverse stakeholders. These skills are vital for driving innovative research, making data-driven decisions, and contributing impactful solutions in data-centric environments.

What are some common challenges faced by data science PhDs when transitioning from academia to industry roles?

Data Science PhDs often encounter challenges such as adapting to the faster pace and collaborative nature of industry projects compared to academic research. In industry, there is a greater emphasis on delivering practical solutions within tight deadlines and working closely with cross-functional teams like engineering and product management. Additionally, data science work in industry may require balancing technical rigor with business impact, often prioritizing actionable insights over exhaustive analysis. Building strong communication and stakeholder management skills can help ease this transition.

What can I do with a data science PhD?

A data science PhD prepares individuals for advanced roles in research, analytics, and machine learning across industries such as technology, finance, healthcare, and academia. Graduates can work as data scientists, machine learning engineers, research scientists, or data analysts, often utilizing programming languages like Python or R and tools such as TensorFlow or SQL. The degree also enables roles involving complex data modeling, statistical analysis, and developing innovative data-driven solutions.

What cities in Virginia are hiring for Data Science Phd jobs?

Cities in Virginia with the most Data Science Phd job openings:

Infographic showing various Data Science Phd job openings in Virginia as of September 2026, with employment types broken down into 6% Internship, 67% Full Time, and 27% Contract. Highlights an 94% In-person, and 6% Remote job distribution.

Senior Associate, Data Science

Mclean, VA β€’ On-site

Information Technology Senior Management Forum
11 - 50 employees

Other

Posted 13 days ago


Key responsibilities

  • Partner with cross-functional teams to identify and quantify risks associated with models

  • Build statistical and machine learning models to challenge existing models and support loss forecasting modernization

  • Contribute to model governance and present model risk insights to executives


Job description

Senior Associate, Data Science

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description:

The Credit Risk Management, Loss Forecasting and Allowance team uses the latest technologies and innovative models and data to forecast and optimize future losses associated with Capital One's credit card portfolio. We partner with model development and software engineering teams to build predictive models and automate insight generation.

As a Data Scientist, you will focus on loss forecasting modernization and data transformation. The qualified candidate will support card loss forecasting: resilience, outlook, allowance and CCAR.

Role Description

In this role, you will:

  • Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
  • Leverage a broad stack of technologies - Python, Conda, AWS, Spark, and more - to reveal the insights hidden within data
  • Build statistical/machine learning models to challenge "champion models" that are deployed in production today
  • Contribute to the model governance of the next generation of machine learning models
  • Flex your interpersonal skills to present how model risks could impact the business to executives

The Ideal Candidate is:

  • Innovative. You continually research and evaluate emerging technologies. You stay current on published state‑of‑the‑art methods, technologies, and applications and seek out opportunities to apply them.
  • Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
  • Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms.
  • Statistically-minded. You’ve built models, validated them, and backtested them. You have experience with a wide array of methods.
  • A data guru. "Big data" doesn’t faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.

Basic Qualifications:

  • Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
    • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics
    • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration

Preferred Qualifications:

  • Master’s Degree in "STEM" field (Science, Technology, Engineering, or Mathematics), or PhD in "STEM" field (Science, Technology, Engineering, or Mathematics)
  • Experience working with AWS
  • At least 2 years’ experience in Python, Scala, or R
  • At least 2 years’ experience with machine learning
  • At least 2 years’ experience with SQL

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full‑time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part‑time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

McLean, VA: $135,600 - $154,800 for Sr Assoc, Data Science

Richmond, VA: $123,300 - $140,700 for Sr Assoc, Data Science

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.

This role is expected to accept applications for a minimum of 5 business days. No agencies please.

Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace.

Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

#J-18808-Ljbffr