Senior Data Scientist

1) Key Responsibilities:

Data Science, AI & Decision Intelligence:

• Develop and deploy predictive models, customer segmentation, propensity models, churn models, profitability models and other advanced analytics solutions.
• Design and implement AI and machine learning solutions including recommendation engines, Next Best Action, conversational AI, agentic AI and decision intelligence capabilities.
• Develop real-time analytics, scoring and decisioning models that can support customer interactions, campaign triggering and operational decision-making.
• Build event-driven models and trigger frameworks that respond to customer, transactional and behavioural events in near real time.
• Perform feature engineering, model training, validation, performance monitoring and model optimization.
• Apply machine learning, statistical modelling, Generative AI and advanced analytics techniques to solve business problems.

Data & AI Platform Collaboration

• Work closely with Data Engineering teams to define AI-ready data models, data marts, feature stores and knowledge repositories.
• Support implementation of real-time data pipelines and streaming data architectures required for event-triggered decisioning.
• Collaborate with Technology teams to operationalize machine learning and AI models into production environments.
• Ensure solutions are scalable, explainable, monitored and production-ready.

Governance & Best Practices

• Maintain model documentation, validation, monitoring and explainability standards.
• Support model governance, AI governance and responsible AI practices.
• Ensure compliance with regulatory and risk management requirements

2) Job Requirements

a) Experience

• 5-8 years experience in Data Science, Machine Learning, AI, Advanced Analytics or Decision Science.
• Experience developing and deploying predictive, prescriptive or AI-driven solutions into production environments.
• Experience working with customer analytics, personalization, recommendation engines, real-time decisioning or event-triggered use cases is highly desirable.
• Experience within banking, financial services, fintech, telco or digital businesses is preferred.

b) Technical Skills

• Strong proficiency in Python and SQL.
• Experience with machine learning, statistical modelling, segmentation, clustering and predictive analytics.
• Experience with Generative AI, LLM applications, RAG architectures, AI agents or conversational AI solutions.
• Experience designing real-time scoring, event-triggered workflows, recommendation engines or decisioning solutions is an advantage.
• Familiarity with Databricks, Microsoft Fabric, Snowflake, Azure ML or equivalent cloud platforms.
• Understanding of MLOps, model deployment, model monitoring and CI/CD concepts.
• Experience working with large-scale datasets, feature engineering and analytics-ready data marts.