Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack - empowering organizations to run AI across multi-vendor environments with centralized governance.

The world’s leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog, LinkedIn, X, and YouTube.

Senior Data Scientist, Singapore

At Dataiku, the Senior Data Scientist role is unlike a typical data science seat. You won't just build models - you'll embed directly with our customers, co-design AI solutions with them, and see your work through initial solution design workshops to production impact. You'll work across a portfolio of industries and use cases, acting as both the technical authority and the trusted advisor inside every account you touch.

This is a role for someone who wants real ownership: end-to-end accountability for solution design, hands-on delivery, and a direct line of sight to business outcomes - not a narrow slice of a pipeline. Alongside the technical work, you'll shape how customers adopt and get value from the Dataiku platform, train their teams and advise their leadership. Just as the non-technical skills are important, so too are the technical: our Data Scientists work in the Dataiku platform every day, primarily in Python and SQL, with occasional work in other languages (e.g., R, PySpark, JavaScript). An ideal candidate is excited to learn complex new technologies and modeling techniques while being able to explain their work to other data scientists and clients alike.

In this role, you will:

• Design and co-develop production-grade AI projects with our customers across a wide range of industries and use cases
• Enable users to discover and master the Dataiku platform through training sessions, office hours, workshops, and ongoing consultative support
• Embed directly with client data, business and engineering teams, contributing to end-to-end solution architecture - from data integration and pipeline design through to production deployment
• Make and defend key architecture trade-offs (native platform functions vs. custom code, batch vs. real-time), balancing client constraints, platform capabilities, and delivery timelines
• Provide strategic guidance to customers and account teams to help drive adoption, value realization, and long-term success
• Contribute to shaping solutions and building compelling proposals, including technical scoping and effort estimation
• Help drive efficient customer engagements by coordinating sprints, prioritizing tasks, and estimating delivery effort

You might be a great fit for this role if you have:

• 8 years of experience in Advanced Analytics & AI, ideally in a consulting or customer-facing data science/AI solution engineering role
• 5 years of hands-on experience with Python and SQL
• 5 years of experience designing, building, and deploying production-grade AI & ML models
• A track record of owning technical outcomes directly with customers - whether through consulting, professional services, or embedded delivery models
• A strong curiosity and appetite for learning new topics, tools, and techniques
• Empathy and collaboration skills, with a genuine eagerness to share your knowledge with colleagues, customers, and the wider community
• The ability to communicate complex concepts clearly to both technical and non-technical audiences

Technical skills that will help you succeed in this role:

• Experience using Large Language Models (LLMs), Agents and Machine Learning to build innovative, real-world applications
• Experience with data visualization, including building applications using frameworks such as Dash, Streamlit, RShiny or JavaScript
• Experience consuming and/or building APIs
• Experience with Data Engineering and MLOps practices
• Familiarity with enterprise data science platforms and tools
• Understanding of cloud architectures, as well as systems such as Hadoop or Kubernetes
• A demonstrated passion for teaching, mentoring, or public speaking
  
  

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