Job Title      : Principal Machine Learning Engineer
Experience  : 12-21 Years
Location      : Any / US / India / LATAM

Job Description :

Project Overview

• The candidate will be working on the Model Development as a Service (MDaaS) initiative,
• Which focuses on scaling machine learning techniques for exception classification, early warning signals,
• Data quality control, model surveillance, and missing value imputation.
• The project involves applying advanced ML techniques to large datasets and integrating them into financial analytics systems.

Key Responsibilities

• Set up Data Pipelines: Configure storage in cloud-based compute environments and repositories for large-scale data ingestion and processing.
• Develop and Optimize Machine Learning Models:
• Implement Machine Learning for Exception Classification (MLEC) to classify financial exceptions.
• Conduct Missing Value Imputation using statistical and ML-based techniques.
• Develop Early Warning Signals for detecting anomalies in multi-variate/univariate time-series financial data.
• Build Model Surveillance frameworks to monitor financial models.
• Apply Unsupervised Clustering techniques for market segmentation in securities lending.
• Develop Advanced Data Quality Control frameworks using TensorFlow-based validation techniques.

Experimentation & Validation:

• Evaluate ML algorithms using cross-validation and performance metrics.
• Implement data science best practices and document findings.
• Data Quality and Governance:
• Develop QC mechanisms to ensure high-quality data processing and model outputs.

Required Skillset

• Strong expertise in Machine Learning & AI (Supervised & Unsupervised Learning).
• Proficiency in Python, TensorFlow, SQL, and Jupyter Notebooks.
• Deep understanding of time-series modeling, anomaly detection, and risk analytics.
• Experience with big data processing and financial data pipelines.
• Ability to deploy scalable ML models in a cloud environment.

Deliverables & Timeline

• Machine Learning for Exception Classification (MLEC): Working codes & documentation
• Missing Value Imputation: Implementation & validation reports
• Early Warning Signals: Data onboarding & anomaly detection models
• Model Surveillance: Fully documented monitoring framework
• Securities Lending: Clustering algorithms for financial markets
• Advanced Data QC: Development of a general-purpose QC library

Preferred Qualifications

• Prior experience in investment banking, asset management, or trading desks.
• Strong foundation in quantitative finance and financial modeling.
• Hands-on experience with TensorFlow, PyTorch, and AWS/GCP AI services