“Technology should solve problems, not create them”.

More than 80% of Data Science projects fail. In order to implement a successful DS project a large budget and a skillful team is not enough. Companies need a Data Science strategy. We are focused on providing guidance to navigate through all the challenges and deliver effective solutions for your projects, from hiring, to define business goals and choose technology and communicate solutions.

Strategic Data Science

With hands-on experience, I help organizations design and implement Machine Learning projects and design a Data Centric Strategy at several stages of development, from designing DS pipelines to training existing DS teams.

An enterprise data science project is highly complex and requires deployment of an interdisciplinary team that involves assembling data engineers, developers, data scientists, subject matter experts and business stakeholders. I help your company build a strategy and a detailed roadmap preparation to deliver impactful DS projects.

Contrary to many consultants, we can provide a hands-on consultancy from years of experienced, from NLP to image segmentation and classification (see our code)..
Beyond technical implementation, I help integrate the long-term strategic goals and define a realistic road map with expected outcomes.

Several types of Data Science:

  • The cosmetic data science
  • Incremental data science
  • transformational data science
  • Foundational Data Science

Data Science projects have to be designed very at business unities.

I will introduce to concepts such as:

  • The Data Science pipeline
  • The Data Virtuous Cycle
  • The Scalability dilemma
  • Humans in the loop: where automation touches manual processes
  • Why simulations can be more important than reality
  • The bottlenecks of Machine Learning innovation
  • Precision is not everything

As a consultant, I focus on the following topics:

  • Identifying the business needs and design an effective roadmap to deploy a ML project
  • Help navigate the technological requirements to deliver value and integrate the outcomes into the business.
  • Prepare the organization for the implications of ML in terms of process requirements
  • Identify opportunities to improve or disrupt business using innovations
  • Introduction to new methods and improving existing data science teams.
  • Verify the data quality, usability and consistency
  • Introduction to advanced machine learning methods

Hands-on Data Science

I have a large experience on some small and large scale projects evolving a wide range of techniques, from Bayesian optimization to Deep Learning for image processing.  More information on section “Hands on Data Science”.

Use Cases