Assistant Manager - Financial Services Risk Management (AI, Machine Learning & Quantitative Analytic)
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.
Location: TBC
Languages: English (Mandatory)
Experience: 3-6 years
Industry Focus: Open - Banking experience (Software, Data Science, or FinTech background welcome)
Job Summary
EY's Financial Services Risk Management (FSRM) practice seeks a technically strong Assistant Manager with expertise in machine learning, data science, and software development.
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, and participate in hackathons, PoCs, and innovation sprints within EY's global AI community.
Skills & 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 maintaining 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.
Seniority level
Mid Senior level
Employment type
Full time
Job function
Finance and Sales
Industries
Professional Services