Machine Learning Modeling Lead
About The Position
The Machine Learning Modeling Lead - Credit Modeling will join the ML Model Innovation team in the banking/fintech digital lending domain. The role leads the development and deployment of machine learning models that support business decisions involving collections, credit risk scoring, fraud detection, customer segmentation, and personalized financial services. The successful candidate will bring strong banking business, data, and domain knowledge across the customer lifecycle, including bureau data, and will collaborate with cross-functional teams while providing technical leadership to junior ML modelers and data scientists.
Requirements
- At least 9 years of applied machine learning model development experience in banking or financial services.
- Strong business knowledge of banking analytics across the retail banking customer lifecycle.
- Hands-on experience leading machine learning projects and teams.
- Strong experience developing, deploying, and monitoring models in production environments.
- Familiarity with collections, underwriting, fraud, and ethical considerations in banking machine learning models.
- Demonstrable leadership ability, problem-solving skills, and people-management skills.
- Master’s degree or similar qualification in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.
- Expertise in Python, SQL, and machine learning libraries including NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
- Knowledge of machine learning techniques including regression, decision trees, ensembles such as XGBoost, GBM, and Random Forest, and unsupervised learning.
- Strong grasp of statistical modeling, optimization, and deep learning techniques.
- Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.
Nice To Haves
- Knowledge of MLOps frameworks such as MLflow, Kubeflow, Airflow, Docker, and Kubernetes.
Responsibilities
- Lead end-to-end machine learning solution development and innovation, including data exploration, feature engineering, model development, validation, deployment, and monitoring.
- Develop robust models that deliver business benefits.
- Support and review junior scientists’ submissions and provide enhancement suggestions.
- Prepare and review documentation, conduct model reviews, and manage model submissions.
- Respond to queries raised by model validation teams.
- Collaborate with implementation teams to deploy models into cloud or on-premises production environments.
- Work with business stakeholders to translate banking-domain challenges into data-driven solutions.
- Guide junior data scientists and engineers on model-development best practices.
- Evaluate new tools, technologies, and frameworks relevant to machine learning in finance.
- Publish internal research and promote innovation and experimentation.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
