Mikael is a specialist in transversal collaborations. During the last 10 years, his expertise lead him to enable innovations in different sectors such as biomedical research, HR-tech and consulting. In order to better identify how value can be created with data, he also regularly publishes online articles about Data Science and AI under the lens of business and client need identification.

Work Experience

Managing Data Science Consultant

2022 - Now
  • In charge of data projects operational excellence in challenging contexts.
  • Scientific leadership on deep learning based innovations (computer vision and generative)
  • Extensive technical expertise for the delivery of industrialised data science projects (reproducible, robust, scale-proof)
  • Coaching of data science consultants
  • Sectorial trends : life science and public services
  • Presales activities to promote data-driven innovation

Data Strategy Consultant

2020 - 2022
Wewyse24 months
Benefiting from an interdisciplinary background, provides expertise on both methodological and technical aspects of data fueled innovation projects.
  • Increase online sales in response to Covid19-accelerated digitalization for a worldwide retail sports industry (recommendation engine).
  • Identify levers and prioritize use cases for a healthcare firm with organizational barriers.
  • Enhance brand visibility:
    • Scientific leadership in an open collaboration project in the media sector.
    • Improve inbound marketing strategy (content creation, e.g. Weblog Wemanity).

Full Stack Data Scientist

2019 - 2020
Clustree acquired by Cornerstone On Demand21 months
Collaborate with data, product and customer success teams to bring pragmatic solutions to build an artificial intelligence based career coach.
  • Showcase technology assets to business teams and improve data capital (web applications).
  • Build market niche expertise: white paper on AI market in human resources (inbound marketing).
  • Lead R&D initiatives to improve user engagement (serendipity in recommendation engines results).
  • Build up the SaaS core functionalities with client feedbacks in an agile continuous deployment environment (update in-production machine learning models).

Data Scientist

2017 - 2018
Kernix24 months
Manage and deliver data science projects from scoping to planning, execution and defense in front of stakeholders - average project duration 2/3 months.
  • Enhance operational efficiency with a minimal viable product client support chatbot for the power market industry.
  • Raise awareness of organisational silos during the development of an error detection pipeline for a major french pharmaceutical industry.
  • Increase funding rate (10%) and lower the risks by replacing a rule-based model with a machine learning model in a credit default application.
  • Coaching of junior data teams (statistical/machine learning models, coding, good practices).

Engineer > PhD

2011 - 2016
Institut Pasteur 2 years + 3 years
In vitro and in silico academic research on RNA biology and infectious diseases in international projects.



  • Value creation strategies (Value Propostion Design, System thinking, OKR)
  • C-level executives and stakeholders communication


  • Leadership & Team work
  • Agile methods
  • Cost management
  • Business intelligence

Scientific & Technical

  • Data Science (mining, modelling)
  • Data & software engineering (Web / SaaS)
  • Biology & Bio-informatics


  • Conceptualization
  • Risk analysis
  • Critical thinking
  • Synthesis (research articles / audits)


Pubmed - h-index: 9 (2021) - research papers: 10

Volunteer Work

Design Team Member

2015 - 2016
Communication development and design thinking of a conference (in the organizing association).


Predicting duration spent by a car on the test bench