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Data Science Phd information

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

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 jobs can you get with a PhD in data science?

A PhD in data science qualifies individuals for advanced roles such as data scientist, machine learning engineer, research scientist, and data science consultant. These positions often require strong programming skills in Python or R, experience with big data tools, and the ability to develop complex models and algorithms. Graduates may work in industries like technology, finance, healthcare, or academia, often in research or leadership capacities.

What is the salary of a PhD in data science?

A PhD in data science typically earns between $100,000 and $150,000 annually in the United States, depending on experience, industry, and location. Senior roles or positions in high-demand sectors can offer higher compensation, often exceeding $160,000. Advanced skills in machine learning, statistical analysis, and programming are highly valued in this field.

What is the 80 20 rule in data science?

In data science, the 80/20 rule, also known as the Pareto principle, suggests that roughly 80% of results come from 20% of the efforts or features. Data scientists often use this concept to focus on the most impactful variables or tasks to optimize model performance and efficiency.

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 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.

Is doing a PhD in data science worth it?

A PhD in data science can lead to advanced roles in research, academia, or specialized industry positions requiring deep expertise in machine learning, statistical analysis, and programming. It typically involves several years of study and research, which can increase earning potential and job opportunities but also requires significant time and effort investment. The decision depends on career goals and whether advanced research or teaching roles align with your interests.
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Infographic showing various Data Science Phd job openings in Delaware as of June 2026, with employment types broken down into 91% Full Time, 8% Part Time, and 1% Contract. Highlights an 90% Physical, 4% Hybrid, and 6% Remote job distribution.
Senior Manager, Data Science - Credit Review

Senior Manager, Data Science - Credit Review

Capital One

Wilmington, DE

Full-time

Posted 5 days ago

Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 135 frontline employees who took The Breakroom Quiz

73rd of 141 rated banks

Job description

Senior Manager, Data Science - Credit Review

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

In Capital One's Credit Review Models, Data and Innovative solutions team, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. We understand that we can't prepare for tomorrow by focusing on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners.

We partner with best-in-class data scientists, analysts, credit risk management experts, and engineers to innovate solutions that directly impact the company's bottom line in a meaningful way. We do it all in a collaborative environment that values individual insight, encourages each associate to take on new responsibilities, promotes continuous learning, and rewards innovation.

Role Description

In this role, you will:

  • Leverage a broad stack of technologies, such as, Python, Conda, AWS, H2O, Spark, and more, to reveal the insights hidden within huge volumes of numeric and textual data

  • Build statistical and machine learning models to challenge the models in production

  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals

  • Partner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers love

The Ideal Candidate is:

  • A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.

  • 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 know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.

  • 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.

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 7 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 plus 5 years of experience performing data analytics

    • A PhD 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

  • At least 2 years of experience leveraging open source programming languages for large scale data analysis

  • At least 2 years of experience working with machine learning

  • At least 2 years of experience utilizing relational databases

Preferred Qualifications:

  • PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics

  • At least 1 year of experience working with AWS

  • At least 5 years' experience in Python, Scala, or R for large scale data analysis

  • At least 5 years' experience with machine learning

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.

Charlotte, NC: $209,000 - $238,500 for Sr Mgr, Data Science


McLean, VA: $229,900 - $262,400 for Sr Mgr, Data Science


Plano, TX: $209,000 - $238,500 for Sr Mgr, Data Science


Richmond, VA: $209,000 - $238,500 for Sr Mgr, Data Science


Riverwoods, IL: $209,000 - $238,500 for Sr Mgr, Data Science


Wilmington, DE: $209,000 - $238,500 for Sr Mgr, 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 theCapital 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.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

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