Assistant Manager - Financial Services Risk Management (AI,Machine Learning & Quantitative Analytic)

Assistant Manager – Financial Services Risk Management (AI, Machine Learning & Quantitative Analytics)

Location: TBC

Languages: English (Mandatory)

Experience: 3–6 years

Industry Focus: Open – Banking experience not required (Software, Data Science, or Fintech background welcome)

Job Summary

EY’s Financial Services Risk Management (FSRM) practice is seeking a technically strong Assistant Manager with expertise in machine learning, data science, and software development.

This role is ideal for engineers and data scientists who want to apply advanced analytics to real-world financial-risk problems — building, training, and deploying models that power credit scoring, fraud detection, early warning systems, and capital forecasting.

You’ll work alongside senior quantitative specialists and risk professionals to transform model concepts into scalable, production-ready solutions using modern data-science toolchains and cloud platforms.

Key Responsibilities

• Machine Learning Model Development
• Develop, train, and optimize supervised and unsupervised learning models using Python, R, or equivalent frameworks.
• Apply algorithms such as regression, ensemble methods, time-series forecasting, anomaly detection, NLP, and deep learning.
• Design modular, reusable model pipelines with clear versioning and reproducibility.
• Data Engineering & Feature Design
• Build and maintain data pipelines for model training and inference using SQL and modern data frameworks (e.g., PySpark, Airflow, Azure Data Factory).
• Conduct feature engineering, data cleaning, and quality assurance for large structured and unstructured datasets.
• Automate model retraining and validation workflows.
• Model Deployment & MLOps
• Deploy ML models into production environments using containerization (Docker), APIs (FastAPI/Flask), or cloud ML services (Azure ML, AWS SageMaker, GCP Vertex).
• Implement monitoring, drift detection, and performance dashboards for live models.
• Collaborate with EY’s technology teams to integrate models into client systems securely and efficiently.
• Collaboration & Development Support
• Work closely with quantitative and regulatory experts to translate conceptual requirements into technical prototypes.
• Contribute to EY’s internal accelerators and reusable AI components for risk analytics.
• Document code, model assumptions, and testing protocols following EY quality standards.
• Innovation & Research
• Explore emerging AI technologies (e.g., transformer models, generative AI, graph analytics) for potential application in financial-risk contexts.
• Participate in hackathons, PoCs, and innovation sprints within EY’s global AI community.
  
  

Skills And Attributes For Success

• Advanced programming skills in Python (pandas, scikit-learn, TensorFlow, PyTorch) or R.
• Proficiency in SQL and familiarity with NoSQL or cloud-native data systems.
• Strong understanding of ML pipeline design, data preprocessing, and model-evaluation techniques.
• Experience with API deployment, containerization, and MLOps practices.
• Solid mathematical and statistical foundation (probability, linear algebra, optimization).
• Curiosity to learn financial-risk concepts while remaining focused on engineering excellence.
• Collaborative, agile mindset with strong problem-solving and debugging skills.
  
  

To Qualify for the Role, You Must Have

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Engineering, or related field.
• 3–6 years of experience in software development, machine learning, or AI engineering (consulting or product environment).
• Demonstrated experience deploying ML models or analytics products in real-world settings.
• Working knowledge of cloud platforms (Azure, AWS, or GCP).
  
  

Ideally, You’ll Also Have

• Experience with version control (Git), CI/CD pipelines, and model registry tools (MLflow, DVC).
• Exposure to RESTful API design, microservices, or front-end data visualization (e.g., Power BI, Streamlit).
• Familiarity with risk or finance data is a plus but not required.
• Contribution to open-source or academic research projects.