Data Scientist

Job Purpose

Develop analytical capabilities and data science algorithms that enable targeted marketing, optimization of processes, and support other functional areas of the bank. Primary focus will be on applying machine learning techniques, performing statistical analysis, and building high-quality prediction and AI systems integrated with the bank’s products and functions.

Key Responsibilities

Strategy and Planning

1. Support Big Data analytics for MY initiatives.
2. Leverage state-of-the-art algorithms to deliver the value of big data to business.

Business and Performance Management

1. Perform exploratory data analysis and model development using state-of-the-art methods, keeping abreast of the latest developments in machine learning and applying them to solve business problems.
2. Select features, build, and optimize classifiers using machine learning techniques.
3. Extend the company’s data with third-party sources of information when needed.
4. Enhance data collection procedures to include information relevant for building analytic systems.
5. Process, cleanse, and verify the integrity of data used for analysis.
6. Perform analysis and present results in a clear and concise manner.
7. Create automated model automation to produce periodic output systems and constant tracking of performance.
8. Build and integrate Large Language Models (LLMs) and LangChain into business processes.

Regulatory Compliance

1. Ensure all Group Consumer operations are in compliance with Group, local, and regional regulations.

Qualifications

Bachelor's Degree or Professional Qualification in the relevant discipline (Financial/Engineering/Computer Science/Actuarial Science/Statistics).

Relevant Work Experience

Candidate must have at least 5-6 years of hands-on experience in statistical modeling using machine learning and AI techniques.

Key Competencies

Technical/Functional Skills

1. Demonstrated experience in applying and implementing machine learning techniques such as neural networks, SVM, recommender engines, and deep learning algorithms (e.g., TensorFlow, PyTorch).
2. Some knowledge of NLP, computer vision, and other generative AI techniques (e.g., GPT, BERT, GANs) is encouraged.
3. Strong knowledge of common data science toolkits like R and Python.
4. Experience with MLOps tools and practices, including version control, CI/CD, containerization (e.g., Docker), and orchestration (e.g., Kubernetes).
5. Familiarity with SQL and visualization tools such as Excel and QlikView.
6. Knowledge of Big Data Tools (e.g., Hive, Impala, Hadoop, and Spark) is an added advantage.
7. Hands-on experience utilizing cloud-based data analytics offerings and services is an added advantage.

Personal Skills

1. Ability to manage AI/ML projects, including model development, testing, deployment, and monitoring.
2. Strong presentation and influencing skills required to put forward solutions/models to solve business problems.
3. Active team player that supports various solution/model initiatives across the bank.
4. Builds a strong culture of excellent service and high performance.