Leverage your Data Assets
Statistical methods offer a powerful, accessible tool for discovery. However, the most sophisticated analysis is useless if it doesn’t answer the right question or if the results cannot be clearly communicated.
I focus on the entire data journey to provide clear, unambiguous guidance for your business. ▼
- Problem definition
- Study and/or experimental design
- Appropriate and meaningful analyses
- Interpretation and visualization of results
Experience shows that throughout these stages, it is important not only to understand what the data can, but also what it cannot tell to avoid later frustrations. Learn more about the services offered.
Experience
Translating data into clear, actionable strategies is my passion. Almost three decades of experience in applied statistics along with an academic background (PhD and postdoctoral degree in statistics) equip me with the necessary width and breadth to address even the toughest statistical problems.
I have worked with some of the world’s leading FMCG, Food, Flavors & Fragrances, and Biostatistics companies. Find out more about my background.
Core areas of expertise▼
- Sensometrics & Consumer Science
Selection and design of efficient sensory and consumer studies, advanced analyses and communication, credentialing/claim support - General Applied Statistics & Biostatistics
Experimental Design (DoE), clinical and non-clinical biostatistics, multivariate methods, computer-intensive approaches - Webtool development
- Data visualization
- Training
Philosophy
A well-defined problem is the key to informative research. This includes actionable problem statements as well as the definition of measurable end points and decision criteria. Efforts spent early on problem definition will always pay off, and often even prevent wasting resources on studies that are not designed to answer the most relevant, or at times even any relevant, question.
Study design and analysis are typically the direct and straightforward consequence of a properly articulated problem statement.
Why statistics in the era of AI?
This is a recurrent question I am being asked. Many will advocate for using AI for all data-related questions (and everything else). My perspective is different from that. In a nutshell: AI and Statistics are complementary, not competitive. While AI relies on huge amounts of mostly observational data and essentially its underlying correlations, statistical methods can work with much smaller (even tiny) datasets if well-designed. It further allows to assess causality by proper experimentation, and to generate insights about new-to-the world products. Statistical thinking and methods are also needed to gauge the quality of the data behind any AI model to understand its limitations and potential bias. In the modern world, we cannot succeed without either.
Why me?
When your product’s success and your brand’s reputation are at stake, you need more than a statistician – you need a strategic partner. That is where I come in:
- I have worked across many different application areas and am at ease to engage with your specific problem at the necessary depth to optimize the return from your quantitative endeavors.
- My ambition is to understand and speak your language, not to make you understand statistical speech, yet without compromising on technical rigor.
- Statistics means managing uncertainty. I will not rest until you understand the potentials and limitations (risks) associated with any conclusions drawn from the data.
- I opt for routine approaches if (and only if) they serve the problem well.
- My own scientific research around needs from real applications; if needed, I develop appropriate methods (and sometimes these are published later on). Purely technical advancements are of limited interest to me.
From innovative product development to ironclad claims support, partner with one of the world’s foremost experts in Statistics and Sensometrics. Data is not just being analyzed, but transformed into a strategic asset.