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Backtesting Jobs in Denver, CO (NOW HIRING)

Raise forecast accuracy and shorten the monthly refresh cycle, using a backtesting and experimentation framework to test methodological changes with rigor. Build tooling and process to catch ...

Lead Forecasting & Financial Analyst

Denver, CO · On-site

$140K - $190K/yr

  • Life

  • Retirement

  • PTO

Raise forecast accuracy and shorten the monthly refresh cycle, using a backtesting and experimentation framework to test methodological changes with rigor. Build tooling and process to catch ...

Lead the full lifecycle of quantitative model development - from ideation and backtesting to production deployment - across portfolio construction, risk, and factor modeling. * Shape AI-driven ...

Lead, Quant AI Investments, TIFIN.ai

Boulder, CO · On-site

$150K - $225K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Lead the full lifecycle of quantitative model development - from ideation and backtesting to production deployment - across portfolio construction, risk, and factor modeling. * Shape AI-driven ...

Backtesting information

See Denver, CO salary details

$43.2K

$105.4K

$154.4K

How much do backtesting jobs pay per year?

As of Aug 20, 2026, the average yearly pay for backtesting in Denver, CO is $105,438.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,900.00 and $122,500.00 per year, depending on experience, location, and employer.

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 skills and qualifications are needed to thrive as a backtesting analyst?

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 are 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 Denver, CO?

For Backtesting jobs in Denver, CO, the most frequently searched job titles are:

Lead Forecasting & Financial Analyst

Guild

Denver, CO • On-site

$140K - $190K/yr

Full-time

Posted 5 days ago


Job description

Guild is hiring a Lead Forecast Modeling Analyst to scale the impact of our Forecasting and Financial Analytics team. You'll own and improve critical components of our most complex and most important forecasting model - working inside a modern, modular forecasting system and experimentation framework to drive step-change improvements in how well we forecast clients' education benefit program.

As a Lead Forecast Modeling Analyst, you will:

  • Develop and improve the usage-based forecasting models for critical components of clients' education benefit with Guild, using advanced statistical and machine learning techniques.
  • Raise forecast accuracy and shorten the monthly refresh cycle, using a backtesting and experimentation framework to test methodological changes with rigor. Build tooling and process to catch correctable model issues.
  • Transfer modeling logic out of individual heads into well-structured, documented, tested code, building redundancy on a business-critical forecast.
  • Partner cross-functionally with Analytics, Data Science, Data Engineering, Product, and Technology teams to improve the data, systems, and infrastructure the forecast depends on.
  • Mentor and guide junior team members, fostering a culture of continuous learning and innovation.
  • Produce insights, analyses, and recommendations to help internal stakeholders understand forecast drivers and the impact on client and internal business metrics.

You are a strong fit for this role if you have:

  • 5+ years of total experience in analytics, data science, forecasting, or financial planning & analysis.
  • Strong experience with SQL and experience transforming and structuring large datasets into reusable formats.
  • Proficiency in Python and/or R, with depth across time-series and machine learning forecasting methods and sound judgment about the tradeoffs among accuracy, interpretability, and stability.
  • Experience operationalizing models - backtesting, validation, monitoring, drift detection, retraining - ideally on a platform such as Databricks with experiment tracking (e.g., MLflow).
  • A track record of getting productive quickly in an existing codebase and improving it collaboratively.
  • Experience with Looker, Tableau, Omni, or similar data visualization tool.
  • Fluency with AI-assisted development tools such as Cursor, Claude Code, or similar coding agents.
  • The ability to translate forecasts, including their uncertainty, into clear recommendations for non-technical stakeholders.

We are committed to equal pay for equal work and believe in compensation transparency. All salary ranges are standardized nationwide and will not vary by region. This role offers a competitive total compensation package, including a base salary of $140,000 - $190,000 and stock options. Compensation offered will be based on a combination of factors such as experience, competencies, and internal equity.
Posting Date: August 14, 2026
*This role will stay open for a minimum of 3 days.