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

GRIT; Singapore; Sur place; SGD 120 000 - 180 000; Plein temps

Résumé du poste

GRIT, an established international business, is expanding its data and AI capability in Singapore. We are seeking a hands-on builder to turn ambitions into practical applications.

You will work directly with business users to shape use cases and deliver solutions through to production, not just prototypes. You will decide what to build, balance business value and complexity, and help adopt data-driven tools across the organization.

Qualifications

• 6–8 years of relevant experience in applied data science or data/AI solution delivery.
• Strong data science foundation with hands-on model development and validation.
• Structured problem-solving: framing problems, testing alternatives, interpreting results.

Responsabilités

• Turn business challenges into clear, solvable problems with stakeholders.
• Build practical solutions using analytics, ML, automation or AI as appropriate.
• Bring rigour by selecting baselines and validation methods; explain uncertainties.
• Collaborate with Head of Data & AI to prioritise opportunities and trade-offs.
• Define required data with Data Engineer and ensure data quality.
• Own adoption and ongoing improvement; monitor performance and usage.
• Help colleagues use AI confidently through workshops, demonstrations and training.
• Explore emerging capabilities and contribute to document-based AI applications.

Connaissances

Python

SQL

Statistical modelling

Communication

Problem solving

Independent work

Description du poste

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.