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Freelance Data Science Startup Jobs in Virginia (NOW HIRING)

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Freelance Data Science Startup information

What is the difference between Freelance Data Science Startup vs Data Analyst?

AspectFreelance Data Science StartupData Analyst
CredentialsRelevant degrees, certifications in data science or analyticsDegree in statistics, data analysis, or related fields
Work EnvironmentIndependent, project-based, remote or on-siteTypically in corporate or organizational settings, often full-time
Employer & IndustrySelf-employed or startup clients across various industriesEmployers in finance, marketing, healthcare, etc.
Search & Comparison IntentLooking for freelance opportunities or startup roles in data scienceSeeking data analysis roles within organizations

Freelance Data Science Startups focus on independent, project-based work involving advanced data modeling and machine learning, often serving multiple clients. Data Analysts typically work within organizations analyzing data to inform business decisions. While both roles require analytical skills, freelance data science startups emphasize entrepreneurship and technical expertise, whereas data analysts focus on operational data insights within a company.

What is a freelance data science startup?

A Freelance Data Science Startup is a small business or entrepreneurial venture where individuals or small teams offer data science services independently, rather than working as full-time employees for a single company. These startups provide solutions such as data analysis, machine learning, predictive modeling, and data visualization to various clients on a project basis. Freelance data science startups often work with businesses that need expertise for specific projects or lack in-house data science resources. They may operate remotely and handle multiple clients simultaneously, allowing for flexibility and diverse experience. This model is popular among data scientists seeking autonomy and a variety of challenging projects.

What unique challenges do freelance data scientists face when working with startups, and how can they manage them?

Freelance data scientists working with startups often encounter challenges such as rapidly changing project scopes, limited historical data, and the need to wear multiple hats. Since startups typically operate in fast-paced environments, priorities can shift quickly, requiring adaptability and strong communication skills. To manage these challenges, it's important to set clear expectations upfront, maintain transparent communication with stakeholders, and design flexible data solutions that can evolve as the business grows. Building strong relationships with both technical and non-technical team members can also help ensure project alignment and successful outcomes.

What skills and qualifications are needed to thrive as a freelance data science startup founder?

To thrive as a Freelance Data Science Startup founder, you need strong expertise in data analysis, machine learning, programming (Python/R), and a solid educational background in statistics or computer science. Familiarity with tools like Jupyter, TensorFlow, cloud platforms (AWS, GCP), and data visualization software, as well as relevant certifications, is highly beneficial. Exceptional communication, client management, and entrepreneurial skills help differentiate successful founders in this space. These skills are crucial for delivering high-quality solutions, winning clients, and sustaining a competitive edge in the evolving data science market.
What are the most commonly searched types of Data Science Startup jobs in Virginia? The most popular types of Data Science Startup jobs in Virginia are:
What are popular job titles related to Freelance Data Science Startup jobs in Virginia? For Freelance Data Science Startup jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Freelance Data Science Startup jobs in Virginia look for? The top searched job categories for Freelance Data Science Startup jobs in Virginia are:
What cities in Virginia are hiring for Freelance Data Science Startup jobs? Cities in Virginia with the most Freelance Data Science Startup job openings:
Infographic showing various Freelance Data Science Startup job openings in Virginia as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Manager, Data Science - Model Risk Office

Capital One

Mclean, VA • On-site

Full-time

Re-posted 20 days ago


Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

93rd of 170 rated banks


Job description

Manager, Data Science - Model Risk Office
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.
Capital One is selectively recruiting for a Manager for a Model Validation team. The individual would report to the Model Risk Office and work closely with the business groups. This position is responsible for validating payment network business models, including fraud risk, Anti-Money Laundering (AML), Counterparty risk, and financial models. Strong communication skills are essential to effectively engage with a diverse group of stakeholders, irrespective of their technical background.
Role Description
In this role, you will:
  • Perform independent model validations for payment network models, including fraud, AML, counterparty and financial models in accordance with regulatory guidanceSR 11-7 and internal model risk policy and standards.
  • Validate fraud and AML modeling approaches, including:
    • Rule-based systems and thresholds
    • Statistical models
    • Machine learning models, (e.g., Gradient Boosting, Random Forecast)
  • Remain on the leading edge of analytical technology with a passion for the newest and most innovative tools
  • Understand relevant business processes and portfolios associated with model use
  • Understand technical issues in econometric, statistical, and machine learning modeling and apply these skills toward developing models and assessing model risks and opportunities
  • Communicate technical subject matter clearly and concisely to individuals from various backgrounds both verbally and through written communication; prepare presentations of complex technical concepts and research results to non-specialist audiences and senior management
  • Maintain the efficiency and accuracy of our models through continuous improvement and application of best practices
  • Develop and maintain high quality and transparent documentation
  • Leverage the latest open-source technologies and tools to identify areas of opportunity in our existing framework

The Ideal Candidate is:
  • Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
  • 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 know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
  • 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 6 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 4 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 1 year of experience performing data analytics
  • At least 1 year of experience leveraging open source programming languages for large scale data analysis
  • At least 1 year of experience working with machine learning
  • At least 1 year of experience utilizing relational databases

Preferred Qualifications:
  • PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
  • At least 1 year of experience working with AWS
  • At least 4 years' experience in Python, Scala, or R for large scale data analysis
  • At least 4 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.
McLean, VA: $197,300 - $225,100 for Mgr, Data Science
Riverwoods, IL: $179,400 - $204,700 for 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 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.
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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