Exploratory data analysis, model selection, feature engineering, hyperparameter tuning, setting up backtesting/evaluation frameworks, and some data engineering and SWE tasks Technical Skills:
Exploratory data analysis, model selection, feature engineering, hyperparameter tuning, setting up backtesting/evaluation frameworks, and some data engineering and SWE tasks Technical Skills:
Creating validation frameworks - synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation * Building dashboards and visualizations that communicate model ...
Creating validation frameworks - synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation * Building dashboards and visualizations that communicate model ...
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Creating validation frameworks -- synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation * Building dashboards and visualizations that communicate model ...
Creating validation frameworks -- synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation * Building dashboards and visualizations that communicate model ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Cybersecurity Data Scientist
Mclean, VA · On-site
Apply rigorous experimentation, statistical analysis, and evaluation methods, including precision/recall, drift, calibration, A/B testing, and backtesting against historical incidents to validate ...
Software Engineer-Data Engineering, Machine Learning (ML)
Arlington, VA · On-site
$131K - $158K/yr
... backtesting against historical logs, and shadow-mode evaluation • Building dashboards and visualizations that communicate model outputs to technical and non-technical stakeholders • Collaborating ...
Software Engineer-Data Engineering, Machine Learning (ML)
Arlington, VA · On-site
$131K - $158K/yr
... backtesting against historical logs, and shadow-mode evaluation • Building dashboards and visualizations that communicate model outputs to technical and non-technical stakeholders • Collaborating ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
... backtesting with live operational outcomes. * Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it ...
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?
| 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 Washington? For Backtesting jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Backtesting jobs in Washington look for? The top searched job categories for Backtesting jobs in Washington are:
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Posted 2 days ago
Job description
We are seeking a Senior Data Scientist with a strong sense of ownership to join our Platform team. This position has career growth opportunities for those seeking to become Lead Data Scientist and/or Team Manager. As a Data Scientist, you will
Use the capabilities of our AI Platform to deploy production solutions for our customers.
Work with customer data and own the development of an AI solution from ideation to production
Leverage integrations with big data frameworks (e.g. Databricks) as needed to develop solutions for customers. Primary Focus: Exploratory data analysis, model selection, feature engineering, hyperparameter tuning, setting up backtesting/evaluation frameworks, and some data engineering and SWE tasks Technical Skills: Foundational knowledge in data science/ML/CS Core Strengths: Choosing appropriate models, analyzing data, communicating business insights Tech Stack: Python data science stack (e.g., pandas, scikit-learn, pytorch, plotly, etc.) Engineering Tasks: Implement model training pipelines, create plots and visualizations to communicate insights, write re-usable modeling code Collaborative Aspect: Works closely with MLEs, other data scientists, product, customer success, QA MUST HAVE: A BA or greater in Data Science or Math or equivalent.
Use the capabilities of our AI Platform to deploy production solutions for our customers.
Work with customer data and own the development of an AI solution from ideation to production
Leverage integrations with big data frameworks (e.g. Databricks) as needed to develop solutions for customers. Primary Focus: Exploratory data analysis, model selection, feature engineering, hyperparameter tuning, setting up backtesting/evaluation frameworks, and some data engineering and SWE tasks Technical Skills: Foundational knowledge in data science/ML/CS Core Strengths: Choosing appropriate models, analyzing data, communicating business insights Tech Stack: Python data science stack (e.g., pandas, scikit-learn, pytorch, plotly, etc.) Engineering Tasks: Implement model training pipelines, create plots and visualizations to communicate insights, write re-usable modeling code Collaborative Aspect: Works closely with MLEs, other data scientists, product, customer success, QA MUST HAVE: A BA or greater in Data Science or Math or equivalent.