Senior Data Scientist

Alliance Bank Malaysia Berhad; Kuala Lumpur; On-site; MYR 180,000 - 280,000; Full time

Job summary

Alliance Bank Malaysia Berhad in Kuala Lumpur seeks a Data Scientist/ML Engineer to design and deploy predictive, prescriptive, and AI-driven analytics solutions. You will work with data engineers to build real-time scoring, event-triggered decisioning, and governance practices to ensure scalable, production-ready models within a banking environment.

Ideal candidates have 5–8 years in data science, strong Python/SQL skills, and experience deploying ML solutions in financial services or digital

Qualifications

• 5-8 years' experience in Data Science, Machine Learning, AI, Advanced Analytics or Decision Science.
• Experience deploying predictive, prescriptive or AI-driven solutions into production environments.
• Experience in customer analytics, personalization, recommendation engines, real-time decisioning or event-triggered use cases is highly desirable.
• Banking/financial services/fintech or digital business experience is preferred.
• Strong proficiency in Python and SQL.
• Familiarity with cloud platforms and MLOps concepts.

Responsibilities

• Develop and deploy predictive, prescriptive or AI-driven solutions into production environments.
• Design real-time scoring and decisioning workflows to support customer interactions.
• Collaborate with Data Engineering to build AI-ready data models and feature stores.
• Establish model governance, validation, monitoring and explainability standards.
• Ensure solutions are scalable, explainable and production-ready.

Skills

Data Science

Machine Learning

AI

Advanced Analytics

Decision Science

Python

SQL

Generative AI

Conversational AI

Tools

Databricks

Microsoft Fabric

Snowflake

Azure ML

CI/CD

Job description

• 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.
• 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.