Senior Data Scientist

SPH MEDIA LIMITED; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

SPH Media Limited is seeking experienced Data Scientists across all seniority levels who combine deep technical expertise with delivering results for a fast-moving media business.

You will lead projects end-to-end, scale ML capabilities, and inform strategic decisions across multiple business lines, driving audience growth and engagement through robust data science solutions.

Qualifications

• Advanced degree required: Master's or PhD in CS/ML/OR/Stats/Math or related field
• Equivalent depth via strong track record or published research encouraged to apply

Responsibilities

• Lead projects from conception to deployment and iteration
• Translate complex data science into practical, high-impact solutions for a fast-moving media business
• Scale ML capabilities and inform strategic decisions across multiple business lines
• Define and track key metrics to evaluate data science initiatives
• Collaborate with data engineering to design reliable data pipelines and model deployment processes

Skills

Python

SQL

ML lifecycle

Education

Master's or PhD in Computer Science/ML/OR/Stats/Math

Equivalent depth through experience/research

Tools

TensorFlow

PyTorch

MLOps tools

Job description

About the role

We are seeking driven Data Scientists across all seniority levels who combine deep technical expertise with a strong focus on delivering results.

In this role, you will lead projects from conception through development, deployment, and iteration, translating complex data science into practical, high-impact solutions that meet the demands of a fast-moving media business. You will be instrumental in scaling our machine learning capabilities, strengthening our AI-driven data infrastructure, and informing strategic decisions across multiple business lines.

This is an exciting opportunity to shape high-impact projects, working closely with senior leadership and delivering tangible value to our core media business.

Roles & Responsibilities

Cross-Functional Partnership

• Serve as a strategic partner to business teams, proactively identifying opportunities where data science can add value and drive competitive advantage
• Work closely with business stakeholders to translate complex challenges into clearly defined, high-impact data science initiatives
• Communicate concepts and technical findings with clarity to non-technical audiences, ensuring stakeholder alignment and informed decision-making

Audience Growth & Engagement

• Ensure that data science outputs have practical, measurable applications, consistently connecting analytical insights to strategies that drive meaningful audience growth
• Leverage behavioural data to uncover actionable patterns that inform content strategy, product decisions, and marketing effectiveness across our media platforms
• Develop and refine personalisation models, recommendation engines, and user profiling capabilities that deepen audience engagement

Solution Development & Delivery

• Design, develop, and maintain robust machine learning models that drive business growth
• Lead data science solutions across their full lifecycle, from initial exploration and hypothesis testing through to model deployment, production monitoring, and continuous iteration
• Define and track key metrics to evaluate the effectiveness of data science initiatives
• Devise and implement intelligent automation solutions, with a focus on integrating advanced AI into our core data stack
• Collaborate with data engineering teams to design and maintain reliable data pipelines that support model training, evaluation, and deployment

Capability & Knowledge Development

• Stay abreast of the latest developments in data science, machine learning and AI, evaluating their practical applicability to our business context
• Conduct applied research to test new approaches, validate hypotheses, and push the boundaries of what our data science capabilities can deliver
• Establish robust monitoring frameworks that detect model drift, data anomalies, and performance degradation, enabling timely intervention
• Champion good data science practices, including model versioning, documentation and governance to build a strong and sustainable data science practice
• Share knowledge and findings across the team, fostering a culture of intellectual curiosity and continuous learning

Who are we looking for?

Educational Qualifications

• An advanced degree (Master's or PhD) in Computer Science, Machine Learning, Operations Research, Statistics, Mathematics or related field
• Candidates without a formal advanced degree who can demonstrate equivalent depth through a strong professional track record or published research are equally encouraged to apply

Industry Experience

• Minimum 8 years of experience building and managing high-performance Data Science teams, with a track record of fostering a culture of rigour, experimentation, and impactful model delivery
• Minimum 10 years of hands-on experience building and deploying production-grade machine learning models with measurable business impact
• Proven ability to navigate ambiguity, take ownership, and deliver scalable solutions in cross-functional, fast-moving commercial environments
• Demonstrated expertise in one or more specialised domains, including recommendation systems and personalisation engines, natural language processing, large language models and retrieval-augmented generation (RAG),knowledge graphs, or time-series forecasting, is highly desired. Candidates who have successfully taken these capabilities from experimentation through to production at scale will be preferred.
• Experience in the media or internet industry is an added advantage

Technical Background

• Strong command of Python and SQL, with hands-on proficiency across core data science libraries and at least one deep learning framework. Coding tests may be required.
• Solid grounding in machine learning fundamentals from evaluation metrics, feature engineering, regularisation to model selection, with practical expertise to navigate the full ML lifecycle from data ingestion through to production monitoring
• Comfortable working with complex, high-dimensional datasets and applying statistical rigour to ensure findings are robust, reproducible, and actionable
• Familiarity with large-scale data processing tools, with practical experience deploying and operationalising models via MLOps tooling