Data Scientist II (Remote) - Special Client Project

Agile Lab is a company founded in 2014 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 our handbook to discover our core values and processes.

💼 The Opportunity:

We are looking for a skilled Data 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 a guaranteed 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: €38.5K - €48.5K

💻 Responsibilities:

• Analyzes business requirements and 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 solutions incorporating software engineering quality standards (SDLC) and data engineering best practices;

• Participates in the technical design of features with guidance;

• Understands and optimizes and monitors model performances;

• Prioritizes tasks with autonomy based on requirements and proper context.

🛠️ Requirements:

• Bachelor's degree in Computer Engineering or Computer Science;

• Previous experience as a Data Scientist or Machine Learning Engineer (+2 years);

• In-depth knowledge of at least one machine learning framework such as Tensorflow, PyTorch or scikit-learn;

• Experience with timeseries analysis is required;

• Good Knowledge of programming languages including advanced Python patterns;

• Familiarity with software lifecycle practices (testing, release, deploy) and git-based workflows;

• Knowledge of MLOps practices;

• Ability to explain ML results and limitations to tech and non-technical audiences;

• Good project management skills;

• Good communication skill;

• Languages: both Italian and English;

• Team Player.

🙌🏻 We offer:

• Career Accelerator: A structured 6-month path leading to direct employment with a top-tier client and a salary step-up;

• Remote or smart working and work life balance;

• Training monthly budget;

• Benefits and corporate welfare programs: meal vouchers, health fund provided, prizes, welcome pack with all the equipment you need to work;

• Agile Nomads experience: opportunity to work for 2 weeks abroad;

• The opportunity to attend one conference per year;

• Inclusive environment where you can be who you really are;

• Stimulating environment oriented to growth, both professional and personal.

😊 How we work:

• We don't like hierarchies: we work as a team;

• We don't like bureaucracies; we prefer sense of responsibility;

• We like data, certainly, so anything that is measurable;

• We want to make a positive change in our industry;

• Empathy, humility, collaboration, and willingness to challenge ourselves are the basis of our work.

Please note:

Only candidates based in European time zones (CEST or similar) will be considered for this position;

🤔 Not sure which level to apply for? Check out our Career Ladder to learn more.