Data Scientist (Spotfire) @HN Services Romania (GP)

HN Services, a company specialized in digital transformation and software development, has 35 years worldwide presence, with offices in Romania (Bucharest), France (Paris, Marseille, Aix en Provence), Portugal (Lisbon and Porto), Spain (Madrid), Luxembourg and United States ( New York) with over 1400 employees.

HN Services Romania has strengthened its local presence since 2006, always being guided by values such as commitment, communication, closeness towards its employees and partners, openness, agility and involvement. We like to recommend ourselves as having the mindset of a company and the agility of a start-up.

With its own Training Center, HN Services Romania contributed to the professional reconversion of more than 300 specialists in Cobol, Java and .NET. We develop projects for various industries from banking to automotive, mainframe (Cobol), Java, .NET, Android, iOS, Salesforce, etc.

Under the slogan #changethecode, we make a difference in the way we work, recruit and in the relationship with our partners and employees. We invite you to find more about us on our LinkedIn page? Facebook or Instagram.

• General role: A data scientist uses statistics and machine learning to analyze, interpret and extract knowledge and insights from complex data, in order to assist the business side with its decision-making.
• Core skills:
• statistics (descriptive and inferential),
• machine learning algorithms (backed up by experience building production models on various types of data),
• SQL for data preparation,
• the 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.),
• automotive industry knowledge.

Upstream of a project:
• Collaborate with the various company players, both business and technical, to imagine innovative analytics solutions and identify synergies between projects.
• check the availability and quality of data sources,
• analyze datasets and experiment new ways of uncovering valuable insight.
During the project phase:
• Participate in the life of a project, with its different members, technical, business, IT, UX, agile.
• Analyze and understand in detail the business problem.
• 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).
In non-project phase:
• Continuous learning.
• Peer validation and support for the approaches and models of data scientists from other projects.
• Technology watch and continuous learning.
• Participate in company's Data acculturation (presentation content and training).
• Participate in the recruitment of new Data Scientists.Upstream of a project:
• Collaborate with the various company players, both business and technical, to imagine innovative analytics solutions and identify synergies between projects.
• check the availability and quality of data sources,
• analyze datasets and experiment new ways of uncovering valuable insight.
During the project phase:
• Participate in the life of a project, with its different members, technical, business, IT, UX, agile.
• Analyze and understand in detail the business problem.
• 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).
In non-project phase:
• Continuous learning.
• Peer validation and support for the approaches and models of data scientists from other projects.
• Technology watch and continuous learning.
• Participate in company's Data acculturation (presentation content and training).
• Participate in the recruitment of new Data Scientists.