Description

You will lead the Model Development as a Service (MDaaS) initiative by scaling machine learning techniques for financial analytics.

Responsibilities

• Build and optimize machine learning models for exception classification, missing value imputation, and market segmentation using unsupervised clustering.
• Develop early warning signals to detect anomalies in multivariate and univariate time-series financial data.
• Construct model surveillance frameworks and advanced data quality control mechanisms using TensorFlow-based validation.
• Configure cloud-based storage and data pipelines for large-scale ingestion and processing.
• Evaluate ML algorithms through cross-validation and rigorous performance metrics while documenting all findings.

Required Skills

• 10+ years of experience in Machine Learning and AI, covering both supervised and unsupervised learning.
• Expertise in Python, TensorFlow, SQL, and Jupyter Notebooks.
• Deep understanding of time-series modeling, anomaly detection, and risk analytics.
• Proven ability to deploy scalable ML models within cloud environments.
• Experience with big data processing and financial data pipelines.
• Strong knowledge of implementing missing value imputation and data quality control frameworks.
• Any Graduate degree.

Preferred Skills

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

Key Skills

Education

Any Graduate