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Backtesting Jobs in North Carolina (NOW HIRING)

Ability to assess model conceptual design, backtesting of model results, assumptions, controls over data flows, model execution, and compliance of model results with intended application by model ...

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.

Model Risk Analyst

NC SECU

Raleigh, NC • Hybrid

Full-time

Posted 13 days ago


Job description

If you are motivated and believe in the credit union philosophy of "People Helping People," join our team!

Position Overview:

Assist in the development, implementation, and maintenance of the Model Risk Management (MRM) program within SECU through the development and validation of statistical models, qualitative models, and models developed with other quantitative algorithms.

Essential Responsibilities:

  • (40%) Execute model validation activities across the model life-cycle including model validations, ongoing performance evaluation, and tracking model findings to ensure models across SECU are conceptually sound relative to their intended use and performing appropriately. Execute end-to-end testing plans for validation and review of SECU's statistical and qualitative models with oversight and guidance from supervisor and other senior validation staff.
  • (30%) Validate the performance and controls of statistical models using provided model development documentation and communications with model developers. Document and present findings to management and model owners.
  • (10%) Provide input for enhancements to the model risk management framework, including maintaining model inventory and model risk rankings.
  • (10%) Develop and maintain effective partnerships within SECU, particularly with model owners, model developers and data analysts.
  • (10%) Assist in implementation of, and adherence to, the MRM Policy and associated model risk SOPs across SECU.

Required Education & Experience (Knowledge, Skills, & Abilities):

  • Bachelors in a quantitative discipline (Economics, statistics, finance, data science or analytics, math, physics, or related field)
  • 3+ years of experience in modeling or analytics
  • Ability to assess model conceptual design, backtesting of model results, assumptions, controls over data flows, model execution, and compliance of model results with intended application by model users.
  • Advanced programming skills in a statistical programming language, such as SAS, R, or Python. Ability to write computer code to perform analysis on complex modeling and analytical challenges and to review code written by others for accuracy and efficiency, with minimal guidance from supervisor.
  • Academic and/or professional understanding of advanced mathematical and statistical modeling techniques, including logistic regression, time series analysis, linear regression, Monte Carlo simulation, Artificial Intelligence/Machine Learning (AI/ML) techniques, etc.
  • Demonstrated ability to contribute to multiple projects simultaneously under guidance from supervisor.
  • Strong oral and written communication skills. Experience contributing to detailed technical validation reports and/or model development documentation.
  • Strong attention to detail and the ability to understand and analyze complex modeling and analytical challenges with some guidance from supervisor and senior staff.
  • Perform job functions independently with some day-to-day oversight from supervisor.

Preferred Education & Experience (Knowledge, Skills, & Abilities):

  • Masters in quantitative discipline
  • Experience in financial services or consulting industry
  • Experience developing or validating models used for CECL, Credit Risk, CCAR/Stress Testing, PPNR, ALM, loan pricing and/or mortgage servicing rights, derivatives, Compliance (BSA/AML/OFAC), Liquidity, or Fraud
  • Subject matter expertise in generative large language models (Artificial Intelligence)

Job Environment & Physical Requirements:

  • Hybrid expectations
  • Sitting for prolonged periods
  • Computer for prolonged periods

SECU provides equal employment opportunity to all qualified persons regardless of race, color, religion, age, sex, sexual orientation, gender identity, national origin, genetic information, disability, veteran status, or other classification protected by law.

Disclaimer

State Employees' Credit Union reserves the right to fill this role at a higher/lower level based on business need.