Keynotes + research + mentorship + industry insights

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Data science is entering a phase where innovation is no longer defined by single model breakthroughs but by connected systems: multimodal architectures, vector databases, retrieval-augmented pipelines, agentic AI, autonomous workflows and scalable MLOps. These shifts are reshaping how organisations design, validate and operate intelligent products — and the expectations placed on teams are rising just as quickly.

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The WiDS Zürich Annual Conference brings these changes together in one place. As part of the global initiative founded at Stanford we highlight the expertise of outstanding women on stage while hosting an event that is open to everyone who wants to understand the future of data science.

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This year’s theme, The Future of Data Science: From Trends to Impact, focuses on translating technological progress into practical results. It is about bridging the gap between experimentation and deployment, connecting research with industry and learning not only from successes but from the failures and frictions that shape real organisational transformation.

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You will hear from people who work on systems that are already shifting the industry: multimodal pipelines, agentic models, real-time inference, AI infrastructure, governance-by-design and the operational realities of scaling AI teams. It is a conference for people who want clarity on where data science is heading and how to turn that direction into impact.

Why it matters

The gap between experimentation and real adoption is widening. Models are getting more powerful but the systems around them — data pipelines, deployment processes, safety layers, governance, ROI frameworks — often lag behind.

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This conference tackles the questions that teams need answers to:

• Which technologies will reshape the next decade

• How to build systems beyond demos and proofs of concept

• Why many AI projects stall or fail

• How to measure ROI in a way leadership actually understands

• What responsible deployment requires in practice

 

Outcome

Participants leave with:

• A clear view of where data science is heading

• Practical insights from real implementations across sectors

• Lessons learned from failures, not just success stories

• Frameworks for scaling from PoC to production

• Approaches for measuring value and impact

• New connections with researchers, practitioners and industry leaders

 

What to expect

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The day is structured around two major thematic blocks — and both combine vision with practical execution.

 

1. AM Session — The Next Frontier: Emerging Tools & Trends

2. PM Session — Data Science in Practice: From Experiments to Impact

3. Panel: AI Transformation — What Works, What Fails and Why

4. Workshops

5. Mentorship Tables & Networking