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Backtesting Jobs in Pennsylvania (NOW HIRING)

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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 job categories do people searching Backtesting jobs in Pennsylvania look for? The top searched job categories for Backtesting jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Backtesting jobs? Cities in Pennsylvania with the most Backtesting job openings:
Backtesting jobs near you
Python Developer- Quant Core Data | Experienced Hire

Python Developer- Quant Core Data | Experienced Hire

Susquehanna International Group, LLP

Philadelphia, PA • On-site

$50.75 - $70/hr

Full-time

Posted 17 days ago

Job description

Overview
Overview
Join our Quant Core Data team to translate mathematical trading logic into high-performance code, and build and scale the data and backtesting infrastructure used by quantitative researchers and strategists. Our team produces data for studies to steadily improve trading behavior and strategies.
In this role, you will:
  • Translate mathematical trading logic into high-performance, optimized code
  • Build and maintain backtesting systems that simulate strategy behavior in production
  • Design and scale our historical data framework for efficiency and robustness
  • Build internal tools to create, analyze, and visualize large datasets
  • Build packages used by expert researchers to evolve our signal generation technology

What we're looking for
  • PhD (graduating by Summer 2026) or postdoc in a quantitative field (e.g., Math, Physics, Statistics, EE, CS, Operations Research, Economics)
  • Experience writing high-performance Python (Cython, pybind11, numba, or similar) and optimizing hot code paths
  • Strong Python data analysis skills (polars/pandas) for large datasets
  • Enthusiasm for data at scale, with strong attention to cleanliness and correctness
  • Comfort translating ambiguous research requirements into robust engineering solutions

About Susquehanna
Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.
What we do
We are experts in trading essentially all listed financial products and asset classes, with a focus on derivatives trading. Through market making and market taking, we handle millions of trading transactions around the world every day, providing liquidity and ensuring competitive prices for buyers and sellers. While our presence in the market is broad, our trading desks are highly specialized, allowing for a deep understanding of unique drivers of each asset class.
If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

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