Senior Associate, Data Scientist, Data Chapter at DBS

About The Role

Business Function

Group Technology and Operations (T&O) enables and empowers the bank with an efficient, nimble, and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability, and innovation. In Group T&O, we manage most of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.

Roles & Responsibilities

Driving Innovation: We are looking for Data Scientists across different levels to drive our journey towards becoming the most intelligent bank in the world.
Stakeholder Consultation: Consult with business stakeholders to understand complex business challenges and devise robust, data-driven solutions.
End-to-End Delivery: Develop, deploy, and productionise data-driven solutions from initial problem definition to final implementation.
Process Improvement: Continuously refine and enhance existing processes for developing and deploying machine learning solutions.
Opportunity Identification: Identify new opportunities for the bank by analyzing vast amounts of both structured and unstructured data.
Model Development: Build predictive models (cross-sell, up-sell, attrition) to optimize customer management and drive revenue.
Hyper-personalization: Develop and deploy hyper-personalization solutions for the consumer bank.
Accountability & Communication: Act as a proactive owner of assigned tasks, ensuring accountability from inception to completion. Communicate complex technical work to business stakeholders with clarity and without jargon, ensuring alignment and understanding.

Requirements

Experience - 6 - 9.5 years.
Technical Foundations: A solid understanding of statistics, mathematical modeling, and machine learning.
Critical Thinking: Demonstrated ability to analyze complex problems, identify root causes, and propose innovative data-driven solutions.
Gen AI Skills: Practical experience in applying Generative AI frameworks like langchain, langflow, google adk etc and prompt engineering skills to enhance data science workflows and business outcomes.
ML Proficiency: Proven experience building Decision Trees, Random Forest, Gradient Boosting, or other ML methods for both classification and regression problems.
Production Experience: End-to-end experience taking solutions from development into a production environment.
Programming Skills: Ability to program in Python, PySpark, and SQL.
Soft Skills:
Proactive & Accountable: A self-starter who takes ownership and accountability for all assigned tasks.
Communication: Exceptional written and verbal communication skills; ability to explain technical outcomes in business-friendly terms, avoiding unnecessary jargon.
Problem-Solving: Strong problem-solving skills, ability to work under pressure, and a positive, resilient attitude.
Learning Agility: A demonstrated ability to self-learn new skills and technologies.
Experience (Good to have): Experience in analytics and modeling within the banking domain is preferred but not mandatory.