Data Science & AI Consulting
Scoping, feasibility studies, and technical strategy for teams exploring what Data Science, Machine Learning, or AI can do for them.
Services
End-to-end Data Science, AI, and Quantum Computing services — from a first conversation about your problem to a solution running in production.
What I offer
ML & AI Model Development
Design and development of predictive, forecasting, classification, and clustering models, and broader AI systems — from classical ML to deep learning, NLP, and LLM applications — tailored to the problem at hand.
End-to-End Deployment
A tailor-made solution designed around your requirements and technology stack — from data gathering and proof of concept, through modelling and software development with a suitable architecture, to APIs, containerization, and CI/CD, so the solution keeps running reliably in production.
Research Collaboration
Theoretical and applied research partnerships bridging academia and industry, from mathematical modelling to publishable results.
Quantum Computing Exploration
Early-stage exploration of Quantum Machine Learning, Quantum Optimization, and Post-Quantum Cryptography for teams looking ahead.
Is Your Enterprise Quantum Ready?
A tailor-made roadmap for quantum adoption — assessing where your organization stands today and what it needs to prepare for Quantum Computing, Quantum Machine Learning, and Post-Quantum Cryptography.
How I work
Two complementary ways of working — one for delivering production systems, one for rigorous research. Same standards of clarity, structure, and communication; different emphasis where it matters.
Industry
End-to-end delivery
From problem framing to a system running in production
Best for product, operations, and applied AI initiatives with clear business goals.
- 01
Requirements & scoping
Clarify objectives, constraints, success metrics, and stakeholders.
- 02
Data collection & exploration
Gather, audit, and understand the data landscape.
- 03
Modelling & algorithm design
Choose and design the right mathematical and ML approach.
- 04
Software architecture & development
Build maintainable systems with a suitable architecture.
- 05
Deployment & CI/CD
Ship reliably — containers, pipelines, and environments.
- 06
Monitoring & iteration
Track performance in production and refine over time.
- 07
Results & stakeholder communication
Present outcomes clearly and keep decision-makers aligned.
Research
Research collaboration
From research question to publishable insight
Best for R&D, academic partnerships, and problems that need first-principles depth.
- 01
Research question
Define the problem with precision and scientific relevance.
- 02
Literature & state of the art
Map prior work and identify the gap to address.
- 03
Theoretical modelling
Formalise the problem with mathematics, physics, or algorithms.
- 04
Experimentation & simulation
Design experiments, run simulations, or analyse data.
- 05
Validation & analysis
Test hypotheses rigorously and interpret results.
- 06
Documentation & publication
Write up methods and findings for papers, reports, or talks.
- 07
Knowledge transfer
Translate results into industry applications when relevant.
Across both workflows
- Rigour before shortcuts
- Clear communication with stakeholders
- Reproducible methods and documented decisions
- Bridge between theory and real-world impact
Let's talk
Have a project in mind, or just want to explore what's possible together? I'd love to hear about it — pick whichever way works best for you.