Financial Data Scientist (Machine Learning)

$140,000 - $160,000/Yr

Carlyle Investment Management - Washington, DC

Full-time - Mid Level

Washington, DC

1,001-5,000 employees

Securities, Commodity Contracts, and Other Financial Investments and Related Activities

About the position

The Financial Data Scientist (Machine Learning) role at Carlyle Investment Management involves leveraging advanced data science techniques, including machine learning and big data, to enhance investment decision-making within the private equity sector. The position emphasizes model development, optimization, and collaboration with a diverse team to create innovative solutions from scratch, rather than maintaining existing systems.

Responsibilities

• Design and implement Machine Learning models for predictive analytics in the private equity sector, encompassing the full lifecycle from exploratory data analysis to deployment.
• Continuously analyze and refine model performance, ensuring robust testing, effective deployment, and ongoing maintenance in a production environment.
• Identify, analyze, and interpret complex data trends within private equity markets, contributing to data-driven decision-making.
• Collaborate with data engineers to enhance data pipelines and automate processing tasks; and with Quant Researchers to validate the backtests.
• Stay abreast of the latest in statistical and ML techniques, particularly those relevant to financial markets and private equity.

Requirements

• Bachelor's degree required; concentration in a STEM field strongly preferred.
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field preferred.
• At least 3+ years of relevant experience in data science or machine learning required.
• Experience in finance or private equity strongly preferred.
• Expertise in Python and its data-related libraries (e.g., Numpy, Pandas, Scikit-learn).
• Deep understanding of ML algorithms for time series analysis and model selection.
• Proficiency in SQL and experience with cloud computing (AWS preferred).
• Demonstrated experience in managing ML models in production, including aspects like scaling and monitoring.
• Strong analytical, problem-solving, and communication skills.

Nice-to-haves

• Familiarity with MLOps tools and practices is a plus.

Benefits

• Comprehensive benefits package including retirement benefits, health insurance, life insurance, and disability.
• Paid time off and paid holidays.
• Family planning benefits and various wellness programs.
• Eligibility to participate in an annual discretionary incentive program based on performance.