Senior Manager

JD : Senior Manager, Data Science (Corporate/SME)

Banking Domain

Exp : 5+ years

Role Overview :

We are looking for a Senior Manager, Data Science, focused on building and scaling data science solutions for Corporate/SME clients. The role requires end-to-end ownership from problem framing and dataset creation to modeling, deployment, and monitoring in production.

Key Responsibilities

* Lead end-to-end delivery of DS/ML solutions for Corporate/SME (including propensity use cases).

* Partner with Business stakeholders to define problem statements, success metrics, and delivery plans.

* Independently extract, prepare, validate data, and build training datasets.

* Design and implement robust pipelines for preprocessing, feature engineering, model training, and evaluation.

* Ensure solution quality: correct labeling design, leakage prevention, validation, and ongoing performance monitoring.

* Collaborate closely with Data Engineering: define requirements, manage dependencies, and troubleshoot pipelines.

* Establish and uphold DS standards within the team (best practices, documentation, review processes, mentoring).

Requirements :

* Strong experience applying Data Science/ML in banking, with exposure to Corporate/SME business

contexts.

* Hands-on proficiency with IBM Cognos to independently extract and prepare data.

* Advanced SQL skills (including window functions) and Python skills (data science packages).

* Deep understanding of the end-to-end ML lifecycle: preprocessing, feature engineering, modeling , evaluation, production/monitoring.

* Solid foundation in ML algorithms and the ability to explain model choices, trade-offs, and limitations.

* Proven experience deploying models to production and applying MLOps practices (e.g., MLflow/registry, CI/CD, monitoring).

* Excellent stakeholder management, communication, and prioritization skills.

Nice to Have :

* Background in recommendations/next best action, segmentation, uplift/causal approaches.

* Strong understanding of labeling design for time-based problems (labeling windows, horizons, leakage prevention).

* Practical experience with Databricks.