Data Scientist

About Inetum, Positive digital flow:

Inetum is an IT services company that provides digital services and solutions and a global group that helps companies and institutions to get the most out of digital flow. The Inetum group is committed towards all these players to innovate, continue to adapt and stay ahead. With its multi-expert profile, Inetum offers its clients a unique combination of proximity, a sectorial organization and solutions of industrial quality. Operating in more than 26 countries, the Group has nearly 27,000 employees and in 2020 generated revenues of €1,965 billion.

Inetum Romania is an important player in the IT services and solutions market in our country, with over 15 years of activity. It is a stable, growing and profitable company with over 500 employees who provides IT consulting services, infrastructure and software development assistance, digital services implementation and support.

In an Agile format, our teams work on cloud initiatives, application development, business intelligence, automation and digitalization projects that contribute to the profit and evolution of our clients. The diversity of our projects offers team members the opportunity for learning and growth. The company had a turnover of 20 million EURO in 2021.

Core skills:
Statistics (descriptive and inferential)
Machine learning algorithms (backed up by experience building production models on various types of data)
TensorFlow
SQL for data preparation
Ability to present the model and pitch it to an outside, non-technical audience (in English)

Desirable skills:
Programming languages (Python preferred)
Cloud platforms (Google Cloud Platform preferred)
Data visualization (Google Data Studio, Spotfire, Tableau, Power BI, Qlik etc.).

Collaborate with various teams in order to imagine innovative analytical processes solutions and identify synergies between projects
Check the availability and quality of data sources
Analyze datasets and experiment new ways of uncovering valuable insight
Participate in the project lifecycle and interact with key members on technical and business subjects
Analyze the business problem and understand it
Data engineering and feature engineering
Create precise and efficient analytical models
Document and present the models and their parameterization
Develop and integrate models in production, from data preparation to prediction
Define and document the tests related to the model (re-training, drift)
Continuous learning and involvement in Data acculturation (training, sharing insight)
Peer validation and support of implementations from other data scientists
Participation in the recruitment of new Data Scientists.