As a Senior Data Scientist within the AI & Data Analytics (AIDA) business unit, you will play a critical role in designing and deploying enterprise-scale AI solutions that drive intelligent decision-making across Singtel's business domains.
This role goes beyond traditional analytics to focus on high-impact AI use cases, including pricing optimization, anomaly detection, and predictive intelligence across network, customer, and operational environments.
You will develop and operationalize advanced AI/ML models and simulation frameworks to:
. Optimize pricing strategies and revenue outcomes
. Detect anomalies and risks in real time across large-scale data systems
. Enable proactive, automated decisioning across enterprise workflows
You will work closely with cross-functional stakeholders (e.g. Network, Finance, Enterprise Business, Product, and Operations) to translate complex business challenges into scalable AI solutions, leveraging advanced machine learning, optimization techniques, and Generative AI where applicable.
Make an impact by:
- Lead Data Science Initiatives: Drive end-to-end AI programs focused on enterprise use cases, including pricing optimization, anomaly detection, and forecasting. Partner with business and technical stakeholders to define problem statements and architect scalable AI-driven solutions aligned with strategic priorities.
- AI Solution Development & Integration: Build and productionize scalable AI pipelines and decision systems, integrating models into enterprise platforms and workflows. Ensure robustness in model governance, explainability, monitoring, and lifecycle management.
- Cross-Domain Data & Insight Generation: Leverage diverse datasets (network, customer, financial, operational) to uncover insights and enable:
. Root cause analysis via anomaly detection
. Scenario testing via digital twins
. Optimization of business levers such as pricing, capacity, and resource allocation - Data Exploration & Experimentation: Enable robust data exploration, insight discovery, and experimentation for lifecycle marketing campaigns. Devise and execute rigorous A/B tests, segmentations, and optimization strategies to continuously improve business outcomes.
- Technical Mentorship & Code Quality: Provide guidance to junior data scientists and collaborate on code reviews to ensure adherence to best practices, maintain high code quality, and promote knowledge sharing within the team.
Skills for Success:
- Bachelor or Postgraduate degree in computer science, mathematics, statistics, or a related field, with at least 3 years of relevant working experience.
- Telecom or Insurance industries Sales / Marketing analytics experience will be an advantage.
- Deep technical and data science expertise, demonstrating proficiency in:
a) Machine Learning & Statistical Modelling: Including linear regression, GLMs, time series forecasting, supervised learning (e.g., gradient boosted trees, neural networks), segmentation, clustering, design of experiments, and causal inference.
b) LLMs & Generative AI: Fine-tuning and evaluation of large language models (e.g., GPT, LLaMA), prompt engineering, red-teaming, and performance monitoring
c) Efficient data manipulation (with skills in SQL, Python, Spark, Hadoop/Hive, and Databricks) and data visualization (using tools like Power BI).
d) Familiar with software engineering best practices, including modular code design, reproducibility, and testing. Proficient with version control tools such as GitHub, GitLab, and Bitbucket - Exposure to cloud platforms (e.g., Azure AWS, GCP) and ML lifecycle tools (e.g., MLflow, Airflow)
- Strong problem-solving skills, with the ability to identify and solve complex business problems through data analysis.
- A team player with a customer-focused mindset, able to work well in a team and passionate about delivering exceptional customer experiences.
- Excellent data visualization and communication skills, with the ability to present insights and findings in a clear, concise, and compelling manner, both in written and verbal form.
- Experience in mentoring junior team members through collaborative problem solving, pair programming, and constructive code reviews.
