Senior Data Scientist, Applied AI Solutions [AI Transofrmation, Business solutions]

GRIT; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

GRIT is expanding its data and AI capability in Singapore to help decision-makers work more efficiently. You will own end-to-end solutions from initial discussions with business users through to deployment, shaping use cases and driving real impact.

This hands-on role emphasizes pragmatism: build practical solutions, justify what to build, and partner with the Data Engineer to ensure governance and adoption across the organization.

Qualifications

• 6-8 years of experience in applied data science, analytics or data/AI solution delivery.
• Strong Python and SQL skills, with ability to build maintainable, reproducible solutions.
• Experience delivering solutions that non-technical stakeholders actually use.

Responsibilities

• Turn business challenges into clear, solvable problems and define successful outcomes.
• Build practical data/AI solutions using analytics, ML, automation or related approaches.
• Bring rigour to modelling, validation and interpretation for business audiences.
• Collaborate with Head of Data & AI to prioritise opportunities and trade-offs.
• Define data requirements with Data Engineer and ensure governed data usage.

Skills

Python

SQL

Data science

Machine learning

Statistics

Problem solving

Communication

Job description

An established international business is building its data and AI capability to improve how people make decisions and get work done. The business is looking for a hands-on builder to turn that ambition into practical applications.

Joining a small, growing team, you will have meaningful autonomy to shape use cases, challenge assumptions and deliver solutions directly with business users. This is not a role where every problem needs a machine-learning model or an AI agent.

Your judgement in deciding what to build—and whether it needs building at all—will matter as much as your technical ability.

It is an opportunity to take work from an initial conversation through to a solution people use, while helping shape how data and AI are adopted across the organisation.

Responsibilities

• Turn business challenges into clear, solvable problems. Work with stakeholders to understand their decisions and workflows, test assumptions and agree what a successful outcome would look like.
• Build practical solutions, not just prototypes. Use analysis, statistical modelling, machine learning, automation or AI according to the problem, taking promising ideas through testing and into everyday use.
• Bring rigour to your work. Choose appropriate baselines and validation methods, investigate unexpected results, and explain uncertainty and limitations in terms the business understands.
• Work closely with the Head of Data & AI. Help prioritise opportunities, shape the delivery approach and make informed trade-offs between business value, complexity and effort.
• Partner with the Data Engineer. Define the data your solutions need, flag quality issues and build on shared, governed data rather than creating disconnected infrastructure.
• Own adoption and ongoing improvement. Gather feedback, monitor performance and usage, and refine solutions as business needs change.
• Help colleagues use AI confidently and responsibly. Support practical workshops, demonstrations, training and the rollout of AI tools across the business.
• Explore emerging capabilities where they add value. Depending on your interests and strengths, contribute to document-based AI applications, MCP tools and agent-enabled workflows.

Requirements

• Around 6-8 years of relevant experience, or equivalent depth, in applied data science, analytics or data/AI solution delivery.
• A strong data science foundation, with hands-on experience developing and validating statistical or machine-learning models using real-world data.
• Evidence of structured problem-solving: you can explain how you framed a problem, considered alternatives, tested your approach and decided what the results meant.
• Strong Python and SQL skills, with the ability to build maintainable, reproducible solutions—not simply run an existing notebook.
• Experience delivering something that non-technical stakeholders actually used, with a clear understanding of your own contribution and its impact.
• The communication skills to ask thoughtful questions, challenge constructively and explain technical findings without relying on jargon.
• Comfort working independently in an evolving environment, while collaborating closely with a small team.
• Willingness to run demonstrations or training sessions and help colleagues adopt new ways of working.