Data Learning

Data Learning are a fantastic boutique engineering shop, applying their skills across multiple domains. They work with their clients to strengthen digital skills, automate processes and develop new data analytics capabilities.

1) Implement tailored data and AI strategies
- Translate business objectives into technical strategy,
- Set KPIs to drive continual process improvement,
- Scope, develop, and deliver end-to-end data platforms including data pipeline management.

2) Build, scale, and lead a cross-functional team
- Create an agile and lean environment with a test and learn culture,
- Work alongside senior members, while mentoring junior staff,
- Full delivery cycle: Agile Scrum, Project Owner, DevOps, dataOps.

3) Experts in data science, coding, and data modelling
- Hands-on approach in C#, python, R, SQL, noSQL,
- Data analytics: Machine learning, predictive analytics, process automation,
- Business Intelligence: Tibco Spotfire, Data virtualisation, Tableau Software, Power BI.

4) Provide a managed data platform as a full service:
- Deploy data platforms in production,
- Automatise your processes,
- Externalise the management of your platform to our experts.

They've demonstrated their agnostic technical expertise across a variety of business and research applications.
1) AI/Machine Learning Research: Stealth MedTech Start-up, Paris
- Lead the development of a scoring algorithm to predict early development of dementia
- Presently working on the publication of a research paper on methodology

2) Digital transformation Project: Investment bank, London
- Conduct a team to develop a large strategic software and data driven platform to scale internal calculations and publications
- Implementation of tailored analytics functions
- The outcome is a 700% increase in productivity with a complete audibility

3) Business & Technical Strategy: Consortium of 6 listed companies, Paris
- Imagine, Design, and define a multi-million-euro digital platform
- Communication with C-stakeholders to maintain a consensus among the members of a consortium with opposite interests