FAQs
What is the main focus of Neotalent Conclusion?
Neotalent Conclusion specializes in providing Information Technology and Engineering services, focusing on increasing the technological capacity of clients and the digital transformation of their application ecosystems.
What type of projects will the Data Scientist be working on?
The Data Scientist will be involved in international projects in the telecommunications sector.
What are the main tasks associated with the Data Scientist position?
The main tasks include solving business problems with data, working cross-functionally to define problem statements, building analytical models, leading the development of optimization models, and communicating recommendations to support decision-making.
What tech skills are required for this role?
Candidates should have 3+ years of scripting experience with Python or Pyspark, familiarity with data manipulation and visualization packages, professional experience with Java, Scala, or Spark, and a good level of communication in English.
Is prior experience necessary to apply for this position?
Yes, candidates should have at least 3 years of experience as a Data Scientist or in a closely related role.
What are the benefits of joining Neotalent Conclusion?
Benefits include working on challenging projects, learning about emerging technologies, the option to work remotely, designing a career plan, accessing continuous training and certifications, and opportunities for internal mobility.
How can I apply for this Data Scientist position?
To apply, please send your CV to the provided email address with the subject line "MVT_DST".
Where is Neotalent Conclusion located?
Neotalent Conclusion has a presence in key locations including Lisbon, Porto, and Madrid.
What type of company is Neotalent Conclusion associated with?
Neotalent Conclusion is part of the Netherlands-based organization Conclusion, which has an ecosystem of over 25 companies and 3,600 employees.
What does MLOps refer to in this context?
MLOps refers to the best practices for deploying and maintaining machine learning models in production environments.