I am an experienced scientific consultant using mainly R and Python to offer data analysis services.
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Originally an industrial pharmacist with experience in both industry and academy.
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I have implemented a quality management system in a startup company, and have worked as a quality assurance manager.
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I worry about decision making in innovative and dynamic contexts as a doctor in industrial engineering.
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Granted for a research grant, implemented business intelligence and patent analytic process in a local university.
With the investment crisis in academic research (and also in R&D in local companies) I set up myself as a full-time freelancer. And discovered the passion for scientific consulting and its opportunity to continually deal with genuinely innovative and challenging projects.
As a scientific consultant, I have
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Collaborated with startups, researchers, and professionals from several fields (medical, marketing, technology).
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brought insights about
◦ factors influencing medical outcomes;
◦ forecasted demand for products;
◦ collaborated for business intelligence process;
◦ assessed technology source alternatives;
◦ surveys to get insights about human perception;
◦ and so on.
Multidisciplinary Background
So my services apply to a variety of fields as a reflection of my multidisciplinary background, from pharma/biotech to managerial and human-science/behavioral fields.
Sounds unrelated fields? Not at all. The rationale for data analysis in these fields is similar. To deal with the complexity of phenomena pertaining to the data, we can get richer insights if we translate accordingly. For example:
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Maybe the data is medical. Physiological and lab parameters are the obvious variables. But the patient’s feelings and perception also matter, and we have perception-based scales in the dataset.
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If we look at the history, we see data analysis (stats) methods transposing the limits of knowledge fields and industries to become authoritative tools in another one… like design of experiments originally developed by Fisher & co-workers considering the Agricultural field; Nightinggale’s Coxcomb chart from Nursing; cluster analysis from anthropology; Neyman’s stratified sampling was first applied for a field we can say social study.
The posts at medium will give you also a glance at this my multidisciplinary perspective.