Senior/Lead Data Scientist

MORGAN MCKINLEY PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

MORGAN MCKINLEY PTE. LTD. is seeking a Senior/Lead Data Scientist to drive large-scale AI/ML programmes, define strategic roadmaps, and translate business priorities into measurable outcomes within a data-driven enterprise.

The role requires hands-on model development, deployment, and governance, with a strong emphasis on scalability, explainability, and stakeholder communication across business units.

Qualifications

• Bachelor's or postgraduate degree in Computer Science, Mathematics, Statistics or related field.
• 5+ years of relevant experience in data science or AI leadership.
• Strong expertise in ML, statistical modelling, and governance.

Responsibilities

• Lead large-scale AI/ML initiatives and define strategic roadmaps.
• Oversee end-to-end model development, deployment, and monitoring.
• Analyse customer and operational data to drive business impact.
• Design and run experiments (A/B tests, uplift modelling) to optimise campaigns.
• Provide technical leadership and mentorship to other data scientists.
• Partner with senior stakeholders to translate insights into actions.

Skills

Generative AI

LLMs

Stakeholder management

Communication

Experimentation

Model evaluation

Education

Bachelor's or postgraduate degree in Computer Science/Math/Statistics

Tools

Python

SQL

Spark

Hadoop/Hive

Databricks

Power BI

GitHub

GitLab

Bitbucket

AWS

Azure

GCP

MLflow

Airflow

Job description

Senior/Lead Data Scientist

A leading telecommunications and digital services provider is accelerating its AI and data analytics capabilities to deliver smarter customer experiences, optimise operations, and unlock new business opportunities. The organisation offers the opportunity to lead impactful AI initiatives at scale within a complex, data-driven enterprise.

Role & Responsibilities

• Lead AI & Data Science Initiatives: Drive large-scale AI/ML programmes, define strategic roadmaps, and translate business priorities into measurable outcomes, leveraging Generative AI and LLMs where relevant.
• Model Development & Deployment: Oversee end-to-end model development, productionisation, integration, monitoring, and continuous improvement, ensuring scalability, explainability, and governance.
• Customer Analytics & Personalisation: Analyse customer behavioural data to drive segmentation, propensity modelling, cross-selling, product targeting, and lifecycle marketing optimisation.
• Experimentation & Optimisation: Design and execute A/B testing, uplift modelling, and other experimentation methods to improve campaign effectiveness and business performance.
• Technical Leadership: Lead, mentor, and develop data scientists while establishing best practices for code quality, reproducibility, documentation, and model lifecycle management.
• Stakeholder Management: Partner with senior business stakeholders and external collaborators, translating complex analytical insights into actionable recommendations aligned with business objectives.

Job Requirements

• Bachelor's or postgraduate degree in Computer Science, Mathematics, Statistics, or a related discipline, with at least 5 - 8 years of relevant experience.
• Strong expertise in machine learning and statistical modelling, including regression, time-series forecasting, supervised learning, segmentation, clustering, experimentation, and causal inference.
• Hands-on experience with Generative AI and LLMs , including fine-tuning, evaluation, prompt engineering, red-teaming, and performance monitoring.
• Proficiency in Python, SQL, Spark, Hadoop/Hive, and Databricks , with data visualisation experience using tools such as Power BI.
• Familiarity with software engineering best practices, version control (GitHub, GitLab, Bitbucket), cloud platforms (AWS, Azure, GCP), and ML lifecycle tools such as MLflow and Airflow.
• Proven technical leadership, problem-solving, and stakeholder management skills, with the ability to communicate complex insights clearly to technical and non-technical audiences.
• Strong customer-centric mindset and ability to translate data science capabilities into tangible business value.