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: 36 years

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

Job Summary

EYs 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.

Youll 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

1. Machine Learning Model Development

• Develop train and optimizesupervised 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.

2. Data Engineering & Feature Design

• Build and maintaindata 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.

3. Model Deployment & MLOps

• Deploy ML models intoproduction 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 EYs technology teams to integrate models into client systems securely and efficiently.

4. Collaboration & Development Support

• Work closely withquantitative and regulatory experts to translate conceptual requirements into technical prototypes.
• Contribute to EYs internal accelerators and reusable AI components for risk analytics.
• Document code model assumptions and testing protocols following EY quality standards.

5. 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 EYs 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

• Bachelors or Masters degree inComputer Science Data Science Mathematics Engineering or related field.
• 36 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 Youll 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.