ZipRecruiter

Log In

1

Backtesting Jobs in Virginia (NOW HIRING)

All JobsBacktesting JobsBacktesting Jobs in Virginia

Backtesting information

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

To thrive as a Backtesting Analyst, you need a strong background in quantitative analysis, statistics, programming (typically in Python or R), and familiarity with financial markets, usually supported by a degree in mathematics, finance, or a related field. Proficiency with backtesting platforms (such as QuantConnect or Zipline), data analysis tools, and version control systems like Git is often required. Attention to detail, critical thinking, and strong problem-solving abilities are key soft skills that help ensure robust model evaluation and development. These skills are vital for accurately assessing trading strategies and minimizing risk in real-world financial applications.

What is backtesting?

Backtesting is the process of evaluating a trading strategy or investment model by applying it to historical market data. This helps traders and analysts see how the strategy would have performed in the past, which can provide insights into its potential effectiveness and risks. While backtesting can help identify strengths and weaknesses, it's important to remember that past performance is not always indicative of future results. The reliability of backtesting depends on data quality, strategy design, and how well it simulates real trading conditions.

What are some common challenges faced when backtesting trading strategies, and how can they be managed?

One common challenge in backtesting trading strategies is the risk of overfitting, where a model performs exceptionally well on historical data but fails in live markets. Data quality and availability can also pose issues, as incomplete or inaccurate data may skew results. To manage these challenges, it's important to use out-of-sample testing, robust data cleaning processes, and to validate strategies on multiple datasets. Collaborating with quantitative analysts and developers can also help ensure the backtesting process is thorough and reliable.

What is the difference between Backtesting vs Quantitative Analyst?

AspectBacktestingQuantitative Analyst
Primary RoleTesting trading strategies using historical dataDeveloping and implementing quantitative models for investment decisions
Required SkillsData analysis, programming, finance knowledgeMathematics, programming, financial theory
Work EnvironmentTrading firms, hedge funds, financial institutionsAsset management firms, hedge funds, banks
CertificationsOften none required, but CFA or CQF helpfulCFA, CQF, or advanced degrees common

Backtesting focuses on evaluating trading strategies with historical data, while a Quantitative Analyst develops models to inform investment decisions. Both roles require strong analytical skills and finance knowledge but differ in scope and responsibilities.

What are popular job titles related to Backtesting jobs in Virginia? For Backtesting jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Backtesting jobs in Virginia look for? The top searched job categories for Backtesting jobs in Virginia are:
Backtesting jobs near you
Principal Associate, Data Scientist - Bank Operations Data Science

Principal Associate, Data Scientist - Bank Operations Data Science

Information Technology Senior Management Forum

Mclean, VA

$60K - $61K/yr

Full-time

Posted 16 days ago

Job description

Team Description

Bank Operations Data Science builds machine learning models that provide core capabilities such as Check/Document Reading, Anomaly Identification, Natural Language Processing of calls, and operational forecasting. Ground‑breaking industry‑leading models are built using neural networks, LLMs, transformer architectures, and agentic experiences.

Role Description

In this role, you will:

  • Partner with a cross‑functional team of data scientists, software engineers, and product managers to deliver a product customers love
  • Leverage a broad stack of technologies—Python, Snowflake, and more—to reveal the insights hidden within large volumes of numeric and textual data
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Translate the complexity of your work into tangible business goals through interpersonal skills
  • Work directly with the call center operations team to streamline call processing, including summarization and automation, to help associates serve customers better
The Ideal Candidate is:
  • Innovative—continually researching and evaluating emerging technologies, staying current on published state‑of‑the‑art methods and applications, and seeking opportunities to apply them
  • Creative—thriving on bringing definition to big, undefined problems, asking questions, pushing hard to find answers, and sharing new ideas
  • Technical—comfortable with open‑source languages, passionate about developing further, and hands‑on experience with open‑source tools and cloud platforms
  • Statistically‑minded—experience building, validating, and backtesting models; interpreting confusion matrices, ROC curves, and working with clustering, classification, sentiment analysis, time series, and deep learning
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:
    • Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
    • 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 3 years of experience performing data analytics
    • PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
Preferred Qualifications:
  • Master’s Degree in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in a STEM field
  • At least 1 year of experience working with AWS
  • At least 3 years of experience in Python, Scala, or R
  • At least 3 years of experience with machine learning
  • At least 3 years of experience with SQL
  • Proven track record of identifying and mitigating risks specifically linked to LLMs
  • Proven ability to create and maintain technical documentation

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

Salary and Benefits

The minimum and maximum full‑time annual salaries for this role are listed below by location.

McLean, VA: $161,800 – $184,600 for Principal Associate, Data Science

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.

Equal Opportunity Statement

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.

Background Check Policy

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.

#J-18808-Ljbffr

Apply