Job Description

Senior ManagerEXL/SM/1501600


ServicesGurgaon
Posted On
07 Oct 2025
End Date
21 Nov 2025
Required Experience
5 Years



Basic Section
Number Of Positions


2


Band


C2


Band Name


Senior Manager


Cost Code


-


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


0.0000 - 0.0000


Complexity Level


Not Applicable


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


Analytics


Sub Group


Analytics - UK & Europe


Organization


Services


LOB


Consulting


SBU


Analytics


Country


India


City


Gurgaon


Center


EXL - Gurgaon Center 38-B




Skills
Skill


MODEL DEVELOPMENT


PYTHON


ML


SQL


Minimum Qualification


B.TECH/B.E


Certification


No data available

Job Title: Machine Learning Modeler Lead - Banking & Financial Services

Location

: [Hybrid - GGN/BLR, WFH]

Department

: Data Science / AI & Advanced Analytics

Employment

Type: Full-time

Job Summary -

We are seeking a highly skilled and experienced Machine Learning Modeling Lead to join our ML Model Innovation team within the banking domain (both retail transactional and lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation and personalized financial services. The Individual needs to have strong knowledge of banking business, data and domain, across the customer lifecycle as well as bureau/external data. He/she will collaborate with cross-functional teams and provide technical leadership to other ML modelers and data scientists.

Key Roles and Responsibilities

-

Minimum Years of Experience: 8+ years in Retail banking Deep business expertise of the different business lines in the Retail banking Credit risk space across the customer lifecycle. Good hands-on knowledge of Banking strategies around Underwriting, Collection & Recoveries, High-Risk Account Management (HRAM) and ECM Strategies. Knowledge of Fraud strategy and analytics in Retail banking domain an added advantage. Possesses end-to-end hands-on ML solution development experience, from data exploration, feature engineering, model development to validation, deployment and monitoring. Develop robust models to solve across the spectrum business problems and drive business benefits. Support and review junior data scientists' submissions and share enhancement suggestions Responsible for documentation / reviews, model reviews and submission Responsible for managing queries raised by the model validation teams and defending the model/solution against internal MRM/audit teams. Collaborate with implementation teams to deploy models into production environments (cloud or on-premises). Work closely with business stakeholders to translate banking domain challenges into data-driven solutions. Guide junior data scientists and engineers on best practices in model development and MLOps Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance. Publish internal research and promote a culture of innovation and experimentation.

Qualifications

Education

:

Master's or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.

Experience

:

Strong business knowledge of banking analytics across the retail banking customer lifecycle. 8+ years of experience in applied machine learning model development in the banking or financial services domain. Hands-on experience leading ML projects and teams. Strong experience with model development, deployment and monitoring in production environments. Familiarity with underwriting, collections, ECM, fraud and ethical considerations in banking ML models.

Skills

:

Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.). Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit. Strong grasp of statistical modeling, optimization, and deep learning techniques. Excellent communication skills and ability to explain complex concepts to non-technical stakeholders
Workflow
Workflow Type


L&S-DA-Consulting