Data Scientist

Kerry Consulting; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Kerry Consulting is seeking an experienced Data Scientist in Singapore to develop innovative data science and AI solutions that drive revenue growth and improve business decision-making. You will collaborate with business stakeholders, product managers, data engineers, and AI engineers to translate commercial challenges into analytical use cases and deploy scalable models.

The ideal candidate has 5–10 years of experience, deep Python and SQL skills, expertise in statistics and ML, and comfort

Qualifications

• 5–10 years of experience applying statistical modelling and ML to business problems.
• Proven track record delivering data science solutions with measurable business impact.
• Strong expertise in Python, SQL, statistical analysis, predictive modelling, and machine learning.
• Hands-on experience with modern AI technologies (Generative AI, LLMs, RAG) is advantageous.

Responsibilities

• Design, develop, and deploy data science and AI solutions to drive business growth and revenue optimization.
• Collaborate with stakeholders to translate commercial challenges into analytical use cases across customer segmentation, pricing, and retention.
• Lead end-to-end data science lifecycle from problem formulation to deployment and monitoring.

Skills

Statistical Modelling

Machine Learning

Python

SQL

Data Visualization

Business Acumen

Tools

Python

SQL

R

Job description

This is a leading financial institution with a strong commitment to leveraging data, advanced analytics, and AI to drive business growth and commercial performance. As part of its continued investment in data and AI capabilities, the organization is seeking to appoint an experienced Data Scientist to develop innovative solutions that enhance customer engagement, optimize revenue opportunities, and improve business decision-making.

This is an exciting opportunity to work at the intersection of business, data science, and AI, applying both traditional statistical methodologies and emerging AI technologies to deliver measurable commercial and revenue impact.

Responsibilities

You will design, develop, and deploy data science and AI solutions that drive business growth, revenue optimization, and customer engagement across the organization. Leveraging traditional statistical techniques, predictive modelling, machine learning, and Generative AI, you will identify commercial opportunities, develop actionable insights, and build scalable solutions that support business decision-making.

Working closely with business stakeholders, product managers, data engineers, and AI engineers, you will translate commercial challenges into analytical use cases across customer segmentation, propensity modelling, cross-selling, customer lifetime value, pricing, and revenue optimization. You will lead the end-to-end data science lifecycle, from problem formulation and experimentation through model development, deployment, and performance monitoring.

You will also explore opportunities to leverage Large Language Models (LLMs), Generative AI, and agentic workflows to enhance customer intelligence, automate analytical processes, and improve business productivity. A key focus will be measuring the commercial effectiveness of data science initiatives through experimentation, business KPIs, and quantifiable revenue outcomes.

Requirements

We are looking for an experienced Data Scientist with 5–10 years of experience applying statistical modelling, machine learning, and advanced analytics to solve complex business problems. You should have a proven track record of delivering data science solutions that generate measurable business, customer, or revenue impact, preferably within financial services or other commercially driven industries.

Strong expertise in Python, SQL, statistical analysis, predictive modelling, and machine learning is essential, together with practical experience in techniques such as regression, classification, customer segmentation, propensity modelling, and experimentation. Hands-on experience with modern AI technologies, including Generative AI, LLMs, Retrieval-Augmented Generation (RAG), or AI-driven analytical workflows, will be highly advantageous.

Candidates should demonstrate strong commercial acumen, with the ability to connect analytical outputs to business outcomes such as revenue growth, customer acquisition, retention, and profitability. Experience working with business and technology teams to take models beyond experimentation into production and operational adoption is important.

The ideal candidate will combine strong traditional data science fundamentals with practical AI capabilities, excellent stakeholder management skills, and the ability to translate complex analytical findings into actionable commercial strategies.