Machine Learning Engineer

This position involves applying advanced machine learning and natural language processing techniques to develop predictive analytics, information retrieval, and document intelligence solutions. The role focuses on use cases within wealth management, private banking, client advisory, compliance, or supervision. The successful candidate will develop production-grade AI systems and scalable data pipelines in a regulated financial institution.

Key Responsibilities
Applying advanced machine learning and natural language processing techniques to develop predictive analytics and document intelligence solutions.
Developing production-grade machine learning and AI systems using Python, SQL, distributed data processing, and software engineering practices.
Evaluating machine learning and AI solutions through quantitative validation, performance monitoring, accuracy measurement, and scalability testing.
Designing and implementing scalable data pipelines and model workflows for large structured and unstructured financial-services datasets.
Preparing model methodology, technical design, performance evaluation, and governance documentation to support model risk management and auditability.
Required Qualifications

Education: An advanced graduate degree in Engineering, Mathematics, Statistics, Computer Science, Actuarial Science, Economics, or a related technical field is required.

Experience: This role requires 1-2 years of experience in quantitative research, financial engineering, data science, or risk analytics within the securities industry. A background in machine learning, hypothesis testing, regression analysis, statistics, text analytics, and predictive analytics with noisy data is necessary.

Technical Skills: Candidates must have experience with data prototyping and coding using tools such as Python or R, Plotly, Shiny, Presto, Tensorflow, Keras, or PyTorch. A strong knowledge of advanced statistical methods, Bayesian learning, pattern recognition, outlier detection, and predictive modeling methods like decision trees and random forests is essential. Knowledge of financial engineering for model development and validation is also required.