Back
Metro location, common mid-senior data science title, and accessible ML skillset increase applicant competition.
Requires wealth management/retail banking domain expertise, making cross-industry transferability limited.
Explicit 6–10 years requirement plus domain expertise and mandatory ML/MLOps tech stack.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop, deploy, and maintain advanced ML and statistical models focused on Wealth Management and Retail Banking business problems.
Analyze large datasets to generate actionable insights, optimize customer journeys, and improve decision-making across banking and wealth management products.
Collaborate with business and technical teams to translate requirements into scalable analytical solutions, including data pipeline and feature engineering optimizations, while driving adoption of AI/ML best practices and model governance.
6 to 10 years of experience in Data Science, Machine Learning, Advanced Analytics, or AI.
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related quantitative field.
Strong experience in Wealth Management, Private Banking, Asset Management, or Retail Banking domains.
Proficiency with Python and SQL; experience with ML frameworks such as Scikit-learn, TensorFlow/Keras, PyTorch, and cloud platforms preferably Azure; hands-on with advanced analytics in production environments.
Experienced in delivering end-to-end data science solutions in Wealth Management or Retail Banking environments with a focus on portfolio analytics, credit risk, customer segmentation, or churn prediction.
Skilled at collaborating with cross-functional teams including product, engineering, and business stakeholders to build scalable AI/ML capabilities and data pipelines.
Familiar with MLOps, model deployment, and emerging AI technologies such as Generative AI and Large Language Models relevant to banking analytics.