Agile Labis a company founded in **** with the mission to create value for its customers in data-intensive environments through customisable solutions that establish performance-driven processes, sustainable architectures and automated platforms based on data governance best practices.
Having delivered over 100 successful Elite Data Engineering initiatives, we have used this experience to create Witboost: a modular, technology-agnostic platform that enables modern organisations to discover, value and produce their data in both traditional environments and fully compliant Data Mesh architectures.
With a highly skilled team of over 260 data engineers based in Europe, Agile Lab helps organisations with their data-driven transformation.
Take a look at ourhandbookto discover our core values and processes.
The Opportunity:
We are looking for a skilledData Scientist II to join a unique project in partnership with a primary industry client.
This role follows a specific "Train & Hire" path:
Phase 1 (First 6 months): You will work as a consultant focused on this specific project, undergoing specialized on-the-job training to master the client's domain and technology stack.
Phase 2 (Hire): Upon successful completion of the 6-month period,you will be directly hired by the client, with aguaranteed salary increase.
For this role, you should have in-depth knowledge of statistics and classical machine learning.
Experience with time series analysis is required.
Familiarity with MLOps practices and collaborative software development workflows is considered a plus.
RAL: €40.0K - €48.5K
Responsibilities:
Analyzes business requirementsand determines a suitable solution autonomously,evaluating if an ML-based solution is feasible;
Good understanding of business requirements;
Develops and fine-tunes models through reproducible experiments;
Builds ML solutionsincorporating software engineering quality standards(SDLC)anddata engineering best practices;
Participates in thetechnical design of features with guidance;
Understands and optimizes and monitors model performances;
Prioritizes tasks with autonomy based on requirements and proper context.