Assistant Vice President, Data Scientist

WHY JOIN US?

• We practice a vibrant & energetic office culture.

• We provide opportunities for career advancement within the company.

• Good performance is always rewarded accordingly.

“It's our people that make Astro Malaysia’s leading entertainment company. We are an inclusive employer, to enable everyone at Astro to be their best. We embrace differences – we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products/services and our community. We also understand and appreciate that diversity is a driver of creativity and innovation, which will make our business more competitive, compelling and profitable.”

JOB RESPONSIBILITIES:

We are seeking an experienced Data Scientist (AVP) to drive advanced analytics, machine learning, AI, and data-driven decision-making across Astro, with primary focus on PayTV, Sooka, Media Sales, Digital Products, Customer Lifecycle Management, and Commercial Growth initiatives. This role will lead the development of end-to-end data science solutions, from problem framing and data discovery to model deployment, monitoring, and business value realization.

The ideal candidate combines strong technical expertise with business acumen and has a proven track record of translating complex data into actionable insights that drive customer acquisition, retention, engagement, revenue growth, and operational efficiency. Experience in Digital Analytics and customer behavioral analytics is added advantage.

Key Responsibilities

• Lead the design, development, deployment, and continuous improvement of predictive, prescriptive, and AI-driven solutions across customer acquisition, retention, churn management, upsell/cross-sell, content consumption, and marketing optimization.
• Own the end-to-end machine learning lifecycle, including:
• Business problem definition
• Data sourcing and feature engineering
• Model development and validation
• Performance monitoring and retraining
• Business impact measurement
• Develop and implement advanced analytics solutions using machine learning, statistical modelling, causal inference, forecasting, optimization, and GenAI techniques.
• Partner closely with business stakeholders to identify opportunities, prioritize use cases, and translate business challenges into scalable analytical solutions.

• Leverage customer, transaction, operational, digital, and third-party data sources to generate actionable insights and recommendations.
• Drive experimentation frameworks such as A/B testing, uplift modelling, customer segmentation, and personalization strategies.
• Collaborate with data engineering teams to establish robust data pipelines, data quality standards, and scalable analytics platforms.
• Lead and mentor data scientists and analysts, fostering a high-performance, data-driven culture.
• Present insights, recommendations, and model outcomes effectively to senior management and cross-functional stakeholders.
• Familiar with digital analytics platforms such as Google Analytics 4 (GA4)  or similar tools

REQUIREMENTS:

Qualifications & Experience

• Master's degree or higher in Statistics, Mathematics, Computer Science, Data Science, Actuarial Science, Engineering, Economics, or a related quantitative discipline.
• 10+ years of experience in data science, machine learning, advanced analytics, or AI-related roles.
• Demonstrated success in delivering end-to-end data science projects from ideation through production deployment and business adoption.

• Strong stakeholder management and leadership skills with the ability to influence decision-making at senior management levels.
• Excellent problem-solving, communication, and presentation skills.
• Advanced proficiency in Python, SQL,  PySpark/Spark, R (optional)
• Experience with large-scale data processing and distributed computing frameworks.
• Familiarity with data engineering concepts, ETL/ELT pipelines, feature stores, and data orchestration tools.
• Strong hands-on experience in:

• Supervised and unsupervised learning
• Classification and regression models
• Time-series forecasting
• Ensemble methods (Random Forest, XGBoost, LightGBM, CatBoost)
•  Clustering and segmentation
•  Recommendation systems
•  NLP and text analytics
•  Deep Learning
•  Generative AI and Large Language Models (LLMs)
•  Retrieval-Augmented Generation (RAG)
•  Customer propensity and churn modelling