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Strong employer backing, popular ML intern title, and likely metro role increase applicant competition significantly.
Core ML engineering skills transfer across industries, but credit-risk/fintech focus raises domain specificity moderately.
Requires demonstrable ML project experience plus Python, ML libraries, and SQL, creating moderately strict filters.
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Develop and maintain end-to-end ML infrastructure, including feature pipelines, training, deployment, and real-time prediction systems handling millions of requests daily.
Build and implement monitoring systems for model performance and drift detection in production environments.
Collaborate cross-functionally to design and improve credit underwriting models using structured and unstructured data, including exploring innovative AI-driven feature generation techniques.
Experience building ML applications beyond toy projects, preferably with real users or open source contributions.
Strong proficiency in Python with ML libraries such as XGBoost, scikit-learn, pandas, and numpy.
Proficiency in SQL for writing complex queries related to feature extraction and data analysis.
Experience with machine learning models using both structured and unstructured data.
Comfortable working in a fast-paced, innovative, and customer-focused fintech environment with focus on credit risk and lending problems.
Capable of owning complex ML engineering tasks end-to-end, including infrastructure, modeling, and deployment, with attention to operational excellence.
Demonstrates strong software engineering fundamentals including modular design, testing, and lifecycle management in production ML systems.