... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness • Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
Lead, Quant AI Investments, TIFIN.ai
Boulder, CO · On-site
$150K - $225K/yr
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
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 frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
Quick apply
... backtesting frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
Lead, Quant AI Investments, TIFIN.ai
Boulder, CO · On-site
$150K - $225K/yr
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
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 frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
... backtesting frameworks to ensure accuracy and robustness * Apply statistical rigor : lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and ...
Backtesting information
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 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 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?
| Aspect | Backtesting | Quantitative Analyst |
|---|---|---|
| Primary Role | Testing trading strategies using historical data | Developing and implementing quantitative models for investment decisions |
| Required Skills | Data analysis, programming, finance knowledge | Mathematics, programming, financial theory |
| Work Environment | Trading firms, hedge funds, financial institutions | Asset management firms, hedge funds, banks |
| Certifications | Often none required, but CFA or CQF helpful | CFA, 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 Colorado? For Backtesting jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Backtesting jobs in Colorado look for? The top searched job categories for Backtesting jobs in Colorado are:
What cities in Colorado are hiring for Backtesting jobs? Cities in Colorado with the most Backtesting job openings:
Full-time
Re-posted 26 days ago
Job description
Job Summary:
Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy, building and maintaining sophisticated predictive and inferential models while conducting deep-dive statistical analyses.
Responsibilities:
• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted
Qualifications:
Required:
• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences
Company:
Klaviyo is an automation and email platform designed to help grow businesses. Founded in 2012, the company is headquartered in Boston, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Klaviyo is looking for a Lead Data Science Analyst to join our GTM Strategic Analytics & Insights team. In this role, you will serve as a senior individual contributor at the intersection of advanced data science, AI/LLM-driven innovation, and Go-to-Market strategy, building and maintaining sophisticated predictive and inferential models while conducting deep-dive statistical analyses.
Responsibilities:
• Build and maintain advanced models: Build and maintain advanced predictive and time-series models: design, train, deploy, and monitor models across use cases such as demand forecasting, capacity planning, deal scoring, and customer propensity; incorporate seasonality, exogenous drivers, and backtesting frameworks to ensure accuracy and robustness
• Apply statistical rigor: lead deep-dive analyses leveraging regression, causal inference, hypothesis testing, correlation analysis, and other statistical methods to surface actionable signals from complex, large-scale datasets
• Develop AI/LLM-powered solutions: architect and implement AI-first analyses and tooling using large language models, prompt engineering, retrieval-augmented generation (RAG), and related techniques to automate insight generation, surface qualitative signals at scale, and augment team capabilities
• Own forecasting and decision systems: Own end-to-end forecasting and operational decision systems, including time-series demand forecasting, capacity planning models (e.g., Erlang-based staffing), and production pipelines that power GTM and Support planning workflows; ensure reliability, scalability, and business adoption of outputs
• Drive customer intelligence: develop and maintain prospect, deal health, archetype, and capacity models that inform GTM strategy, planning, and growth initiatives
• Define the measurement framework: identify, create, and steward benchmarks and metrics that meaningfully represent growth, engagement, and success outcomes
• Communicate with impact: distill complex analyses into clear, cohesive narratives with executive-ready materials that drive decisions at the senior leadership level
• Collaborate cross-functionally: partner with Systems & Engineering, GTM Operations, Rev Ops & Planning, Product, Business Intelligence, Data Science, and Finance to ensure analytical solutions are integrated, scalable, and trusted
Qualifications:
Required:
• 6+ years of professional experience in an advanced analytics or data science role; SaaS experience strongly preferred
• Deep expertise in statistical inference and modeling, including supervised techniques (regression, classification, gradient boosting, decision trees) and unsupervised techniques (clustering, PCA, anomaly detection, topic modeling)
• Hands-on experience designing and deploying AI/LLM-based solutions, including prompt engineering, fine-tuning, RAG pipelines, or LLM-integrated analytics workflows; you approach new problems with an AI-first mindset
• Familiarity and experience with distributed coding projects, including using Git for code management.
• Advanced proficiency in Python (pandas, numpy, scikit-learn, xgboost, statsmodels, and LLM/AI libraries such as LangChain, OpenAI SDK, or HuggingFace) and SQL; working knowledge of DBT
• Own and scale end-to-end data pipelines, including orchestration with Airflow and transformation/modeling with dbt; design reliable, testable, and modular workflows that support production-grade analytics and machine learning use cases, with a focus on performance, data quality, and maintainability.
• Develop and iterate on time-series forecasting frameworks using approaches such as ARIMA/SARIMAX, ETS, MSTL, and machine learning-based models; evaluate performance through rigorous backtesting and continuously improve model accuracy and business applicability
• Experience building data visualizations and dashboards across platforms such as Tableau, ThoughtSpot, matplotlib, seaborn, plotly, or similar tooling.
• Strong project ownership: experienced operating to a roadmap, managing milestones and deliverables, and delivering high-quality work product in a timely manner
• Comfortable with autonomy and ambiguity, with a proactive orientation toward identifying and solving problems before they're fully defined
• Excellent written and verbal communication skills, including experience preparing materials for executive audiences
Company:
Klaviyo is an automation and email platform designed to help grow businesses. Founded in 2012, the company is headquartered in Boston, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About Klaviyo
Sourced by ZipRecruiter
Industry
Marketing
Company size
1,001 - 5,000 Employees
Headquarters location
Boston, MA, US
Year founded
2012