The Junior ML Engineer will support the Company's data science function by developing, validating, and maintaining machine learning models and the data pipelines behind them. This is a hands-on, applied role: you will work with real data on problems that directly shape the product and the business, and you will be expected to explain what your models do and why they can be trusted.

You will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on.

This role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering.

Key Responsibilities

• Develop, test, validate, and maintain machine learning models under the guidance of senior team members.
• Build and maintain data pipelines and analytical datasets on the Company's cloud data platform.
• Evaluate model performance rigorously and document assumptions, methods, and limitations.
• Support statistical analysis, forecasting, and experimentation to inform business decisions.
• Present technical findings clearly to non-technical audiences.
• Contribute to standards for model documentation, validation, and monitoring.

Qualifications

• Bachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus.
• Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code.
• Hands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar)
• Strong SQL, including window functions and multi-table joins.
• Solid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem.
• Hands-on experience with: 
• Gradient-boosted trees (XGBoost, LightGBM)
• Logistic regression, support vector machines, k-nearest neighbors
• Clustering methods (k-means and others)
• Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection
• Experience taking a model from development into a scheduled or production environment
• Docker, CI/CD, and workflow orchestration experience
• Ability to explain model behavior, including feature importance, calibration, and limitations.
• Ability to gather and present technical results to a non-technical audience.
• Proven experience as a machine learning engineer or in a similar role is a plus.
• Fintech, trading, or financial services background is a plus.