Job Title

Key Responsibilities- Engage with clients to understand their business objectives and challenges, providing data-driven recommendations and AI/ML solutions that enhance decision-making and deliver tangible value. - Translate 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, TensorFlow, 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 our services and solutions to our clients at various stages of engagement such as pre-sales, sales, and delivery 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- 4 to 7 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/Asset 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, TensorFlow, 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 - Ability to bridge technical solutions with business impact and drive value through data science initiatives