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Backtesting Jobs in Maryland (NOW HIRING)

Beacon Health System

Quantitative Analyst

Baltimore, MD · On-site

$150K/yr

Familiarity with backtesting frameworks and standard performance/risk metrics (Sharpe, drawdown, turnover, etc.). * Familiarity with quantitative finance concepts and financial instruments. * Working ...

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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.

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What cities in Maryland are hiring for Backtesting jobs? Cities in Maryland with the most Backtesting job openings:
Backtesting jobs near you
Infographic showing various Backtesting job openings in Maryland as of July 2026, with employment types broken down into 1% Internship, 90% Full Time, 7% Part Time, and 2% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.
Quantitative Research Analyst

Quantitative Research Analyst

JEG Search LLC

Baltimore, MD • On-site

Other

Posted 8 days ago

Job description

Quantitative Research Analyst

As a Quantitative Research Analyst, you will work at the intersection of quantitative finance, data, and machine learning. You will design, prototype, and productionize models — both machine learning and classical statistical / financial — that power the research delivered to our subscribers. You’ll partner with senior quantitative analysts to take ideas from hypothesis to backtest to publication-ready research.



Qualification

sRequire

  • dBachelor’s degree in Computer Science, Mathematics, Quantitative Finance, Physics, Engineering, Financial Engineering, Statistics, or a similar discipline
  • .3–5 years of relevant work experience in quantitative finance, data analytics and modeling, or related fields
  • .Hands-on experience building, validating, and deploying both machine learning models (e.g., scikit-learn, XGBoost, PyTorch or TensorFlow) and classical statistical / econometric models (e.g., statsmodels, time-series methods), with the judgment to choose the right tool for the problem
  • .Strong programming skills in Python, including pandas, NumPy, and the scientific stack; comfort writing modular, testable code and using Git
  • .Proficiency with SQL and relational databases (PostgreSQL preferred), including complex joins, window functions, and query performance tuning. Experience designing or contributing to data staging and transformation workflows
  • .Experience working with financial time-series data and an understanding of common pitfalls (look-ahead bias, survivorship bias, regime changes)
  • .Familiarity with backtesting frameworks and standard performance / risk metrics (Sharpe, drawdown, turnover, etc.)
  • .Familiarity with quantitative finance concepts and financial instruments
  • .Working knowledge of financial statements and fundamental data sufficient to incorporate them as model features
  • .Excellent communication skills and the ability to collaborate effectively within a dynamic team

.Nice to Hav

  • eExposure to cloud data infrastructure on AWS
  • .Strong academic or professional background in quantitative finance


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