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Backtesting Jobs in New York (NOW HIRING)

Quantitative Developer Intern

New York, NY ยท On-site

$21 - $27.50/hr

Build systems for data processing, strategy research, backtesting, simulation, and performance analysis. * Assist in developing trading infrastructure, market data pipelines, and execution tools.

Quantitative Developer Intern

New York, NY ยท On-site

$21 - $27.50/hr

Build systems for data processing, strategy research, backtesting, simulation, and performance analysis. * Assist in developing trading infrastructure, market data pipelines, and execution tools.

End-to-end development: alpha idea generation, data processing, strategy backtesting, optimization and production implementation * Identify and evaluate new datasets for stock return predictions

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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.

What cities in New York are hiring for Backtesting jobs? Cities in New York with the most Backtesting job openings:
Senior Quantitative Research Manager

Senior Quantitative Research Manager

MIO Partners

New York, NY โ€ข On-site

Other

Posted 12 days ago


Job description

Team

MIO takes a team-based approach to everything we do. Over decades, we have created distinctive investment frameworks, systems, and processes. We seek a colleague who can use those institutional capabilities to create value for our investors, and assist us in continuously improving our capabilities.

A majority of MIO's active assets under management are invested with external third-party managers (i.e., hedge funds and other alternative investment managers). These third-party investments span a wide range of strategy areas, including equities, global macro, quantitative, multi-strategy, credit, commodities, and fixed income.

A minority of MIO's active assets are deployed through MIO's own in-house macro trading strategies, which are supported by MIO's deep macroeconomic and cross-asset class market research. Our asset class coverage spans global rates and government bonds, commodities, foreign exchange, and global equity and corporate credit indices.

In both activities, the portfolio management team is leveraged by MIO's robust proprietary analytics platforms, in-house data, and experienced support team.

Position

The SQRM will serve as a team-wide expert driving enhancements of MIO quantitative research methodologies and infrastructure. You will collaborate with various Portfolio Managers (PMs) and Investment Associates in optimizing systematic strategies, implementing rigorous back-testing, and applying quantitative approaches to a variety of investment problems. At any point in time, the SQRM will be working on a portfolio of projects, sharing their time across different asset classes. Depending on the specific project, the SQRM could be entirely or partially responsible for execution, or act as a consultant to the relevant PM.

Primary responsibilities

  • Drive enhancement of MIO systematic investing capabilities:
    • The SQRM will work closely with PMs to develop backtesting tools tailored to their specific strategy and asset class needs. While front-end workflows may vary to suit individual use cases, the SQRM will ensure that the underlying backtesting logic and architecture remain as consistent as possible across implementations-promoting alignment, robustness, and maintainability without compromising on flexibility or quality
    • Collaborate closely with PMs to analyze, refine, and optimize existing or new quantitative investment strategies (e.g., by improving dynamic weighting of signals)
    • Establish rigorous testing, validation, and risk management protocols to maintain consistency, transparency, and reliability in systematic strategies
    • Drive the adoption of advanced statistical techniques, as needed
  • Support PMs / CIO in applying the quantitative toolkit to investment problems. Examples might include:
    • Utilize Monte Carlo simulations to model investment outcomes and evaluate strategy robustness under varying market scenarios
    • Apply factor modeling and attribution analysis to help improve investment decisions
    • Implement portfolio construction techniques to optimize risk-return profiles and enhance diversification
    • Model tail-risk scenarios and enhance stress testing framework
  • Guide and mentor research analysts to build internal expertise and continuously elevate team quant & coding capabilities; foster a collaborative environment and the emergence of a 'quant culture' within the team
    • Develop and share best practice guides and applied research papers, and engage the Investment Associates team through regular discussions
    • Advise and oversee Investment analysts coding practices, e.g., hold 'codebase review' sprints in coordination with relevant PMs

These activities will involve working with a varied set of Investment MIO team members, such as investment associates, developers, research managers, PMs, and CIO.

Desired background

  • Significant experience as a quant
    • Exceptional academic background: PhD in quantitative fields (Mathematics, Statistics, Physics, Engineering, Computer Science) strongly preferred
    • 8-12 years of total professional experience as a quantitative researcher and manager of researchers in top tier institutions, ideally both on the buy side and sell side
    • Experience in building systematic investment capabilities (including backtesting infrastructure) in high stakes environments
  • Skillset as a research leader who "makes things happen"
    • Intellectual curiosity and passion for solving problems; continuous learning mindset
    • Distinctive problem solving and analytical skills, applied throughout their career to a variety of investment problems
    • Mastery of the quant toolkit (advanced statistical modeling, derivatives pricing. risk modeling, high performance numerical methods)
    • Excellent coding skills and proficiency in modern data science tools stacks (NumPy, pandas, scikit-learn, TensorFlow, PyTorch)
    • Proficiency with AI tools such as Cursor, GPT, Claude and similar LLM-based assistants to accelerate research
    • Proven ability to work with a complex set of stakeholders (developers, researchers, traders, PMs, senior management) to deliver high-quality, timely results
    • Outstanding written and verbal communication skills; able to interact both with technical and non-technical investment professionals; ties together technical depth with clear business-oriented insights
  • Affinity with MIO values and priorities
    • Desire to work in a values-oriented environment focused on maximizing returns for our investors
    • Placing best interests of our investors above personal objectives
    • Strong work ethic and drive necessary to out-think the competition

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Applicants must be authorized to work in the U.S. without the need for employer-sponsored work authorization, now or in the future.