Senior Data Scientist

We are seeking a Senior Data Scientist to lead the development of high-impact Machine Learning applications that drive the bank's core risk, revenue, and analytical strategies. This role is critical to ensuring our predictive models ranging from Credit Risk frameworks to hyper-personalization engines are robust, scalable, and integrated into the bank's digital ecosystem. While the primary focus is on classical and deep learning applications, the role will also explore Agentic Al to automate complex financial workflows.

Key Responsibilities:

• Credit Risk & Predictive Intelligence: Lead the design and deployment of production-ready Credit Risk scoring models to enhance the bank's lending precision.
• Pattern Mining & Behavioral Analytics: Apply unsupervised learning, clustering, and association rule mining to financial data to uncover hidden spending trends and emerging customer segments.
• Growth & Personalization Engines: Develop and optimize \"Next Best Action\" (NBA) models for upsell and cross-sell initiatives to maximize customer lifetime value.
• Rigorous Experimentation: Design and execute A/B and multivariate tests to validate model performance and measure the incremental lift of Al-driven business interventions.
• Agentic Al Integration: Explore and implement secondary Agentic Al frameworks (e.g., RAG-based systems) to assist in automating specific financial reasoning tasks.
• Mentor & Guide the Data Team: Provide technical leadership, conduct code reviews, and mentor Data Scientists to ensure high engineering standards.
• Maintain Engineering & Quality Standards: Ensure strict data governance, model monitoring, and best practices in version control and CI/CD for all ML pipelines

Qualifications:

• Bachelor's degree in Computer Science, Statistics, IT, or a related discipline.
• 5+ years in a hands-on data science or machine learning role.
• Previous roles: Data Scientist, Senior Data Scientist, Lead Data Scientist, or Senior ML Engineer.
• Experience in the finance domain is highly desirable.

Technical Skills:

• ML & Statistical Modeling: Expert-level Python, Pandas, SQL, Spark, and Scikit-learn.
• Deep Learning Frameworks: Proficiency in Keras, PyTorch or TensorFlow.
• Agentic Al (Secondary): Familiarity with LLM orchestration (LangChain/LangGraph) and Vector Databases.
• Data Platforms: Experience with GCP, BigQuery, and Hadoop.
• MLOps/Infra: Docker, Kubernetes, and Airflow for model orchestration.

Soft Skills & Leadership:

• Ability to fit into the Combank organization & IT culture.
• Excellent attention to detail and analytical skills, specifically regarding model lineage, bias, and governance. 
• Strong communication skills to translate complex model metrics into actionable business insights for stakeholders.