Data Scientist

SPH MEDIA LIMITED; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

SPH MEDIA LIMITED in Singapore is looking for Data Scientists across all seniority levels to lead projects from conception to deployment, focusing on delivering high-impact solutions for a fast-moving media business.

The ideal candidates should have a Master's or PhD in a relevant field and a minimum of 6 years of experience in machine learning and data science. Key skills include a strong command of Python and SQL. Opportunities for career development and impactful projects await the right applicants.

Qualifications

• 6+ years of experience in building and deploying production-grade machine learning models.
• Ability to navigate ambiguity and deliver scalable solutions in fast-moving environments.
• Strong command of Python, SQL, and data science libraries.

Responsibilities

• Lead projects from conception through deployment and iteration.
• Serve as a strategic partner to business teams for data initiatives.
• Design and develop robust machine learning models for business growth.

Skills

Machine Learning

Python

SQL

Deep Learning

Data Analysis

Education

Master's or PhD in Computer Science or related field

Tools

Core data science libraries

MLOps tooling

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 6 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