ABOUT SHAHAF KLEIN
A scientist who speaks business. A business strategist who understands the models.
My work sits at the intersection of science, business strategy and applied artificial intelligence.
Three perspectives, one approach
Science
My original training is in chemistry and scientific research. It taught me to work rigorously with evidence, uncertainty, experiments and data.
Business
I hold an MBA in Marketing & Strategic Management. That means I do not look at a model in isolation: I look at the customer, process, economics and decision it is meant to improve.
AI
I completed advanced Data & AI training and work with applied machine-learning approaches including recommendation systems, forecasting, classification, clustering, anomaly detection and NLP.
Why that combination matters
A technically impressive model is not automatically a useful business solution. The real work is deciding what should be predicted or optimized, whether the available data can support it, how the result will be used, and how success will be measured.
That is the role I aim to play through Data2Profit: translating between business questions and technical possibilities, and helping organizations avoid both AI hype and unnecessary complexity.
Data2Profit at a glance
Data2Profit is an applied AI consultancy founded by Shahaf Klein and based in Grenoble, France. It helps businesses and organizations identify, assess and implement practical AI opportunities.
- Focus: applied AI for measurable business problems, not AI for its own sake.
- Approaches: forecasting, recommendation systems, classification, clustering, anomaly detection, NLP, computer vision and generative AI when appropriate.
- Services: AI Opportunity Diagnostic, custom AI solutions, speaking and workshops, and ongoing support for deployed solutions.
- Languages: English, French and Hebrew.
- Geography: based in Grenoble and working with organizations in France and internationally.
For concise answers about Data2Profit and applied AI, see the Applied AI FAQ.
How I work
- Business-first: start with the problem, not the algorithm.
- Evidence-based: be clear about what the data can and cannot support.
- Practical: prefer a useful solution over unnecessary technical complexity.
- Transparent: distinguish realistic opportunities from assumptions and hype.
