AI​/ML Scientist

Key Responsibilities
Support in translating business needs - particularly within financial services domains such as marketing, risk, compliance and customer lifecycle management into well-defined machine learning problem statements and solution workflows.
Solve business problems using analytics and machine learning techniques:
Conduct exploratory data analysis, feature engineering, and model development to uncover insights and predict outcomes.
Develop and deploy ML models, including supervised and unsupervised learning algorithms and model performance optimization.
Design and implement scalable, cloud-native ML pipelines and APIs using tools like Python, Scikit-learn, Tensor Flow, and PyTorch.
Collaborate with cross-functional teams to deliver robust and reliable solutions in cloud environments such as AWS, Azure, or GCP.
Be a master storyteller for your projects and deliverable using data-driven insights.
Stay current with developments in AI, ML modelling, and data engineering best practices, and integrate them into project work.
Mentor junior team members, provide guidance on modelling practices, and contribute to an environment of continuous learning and improvement.

Job Requirements
2 to 5 years of relevant experience in building ML solutions, with a strong foundation in machine learning modelling and deployment.
Strong exposure to banking, payments, fintech or Wealth management domains, with experience working on problems related to:
Marketing analytics for product cross-sell/up-sell and campaign optimization
Customer churn and retention analysis
Credit risk assessment and scoring models
Fraud detection and transaction risk modeling
Customer segmentation for personalized targeting
Experience in developing traditional ML models across business functions such as risk, marketing, customer segmentation, and forecasting.
Bachelor’s or Master’s degree from a Tier 1 technical institute or MBA from Tier 1 institute
Proficiency in Python and experience with AI/ML libraries such as Scikit-learn, Tensor Flow, PyTorch.
Experience in end-to-end model development lifecycle: data preparation, feature engineering, model selection, validation, deployment, and monitoring.
Eagerness to learn and familiarity with developments in Agentic AI space
Strong problem-solving capabilities and the ability to independently lead tasks or contribute within a team setting
Effective communication and presentation skills for internal and client-facing interactions