DATA SCIENTIST (HYBRID)
Portuguese company hires for hybrid work
Location:
Lisbon / Portugal
Work model:
60% Remote and 40% Presential
Language Requirements:
English and portuguese
Seniority: 6
years of experience
Instructions: Please send your CVs in English and make sure to include all skills and experience that match the requirements of the opportunity. This will significantly increase your chances of success.
As a Senior Data Scientist you will design build and deploy advanced statistical and machine learning models that generate measurable business impact. You will collaborate closely with business stakeholders data engineers and product teams to transform complex data into actionable insights and scalable analytical solutions.
What Youll Bring
6 years of experience in advanced analytics statistical modeling or machine learning including at least 3 years in a Data Scientist role.
Hands-on experience developing models in Python using libraries such as scikit-learn pandas NumPy TensorFlow and PyTorch.
Strong foundation in mathematics probability and statistics.
Practical experience with cloud-based data science platforms (AWS preferred; GCP/Azure also valued).
Proven ability to work with complex real-world datasets to deliver business value (cost reduction revenue growth operational efficiency).
Experience working in Agile or cross-functional product teams.
Excellent problem-solving experimentation and communication skills with the ability to explain technical results to non-technical stakeholders.
What Youll Do
Develop validate and deploy statistical and machine learning models to solve business challenges (e.g. demand forecasting optimization pricing customer behavior).
Source clean and analyze structured and unstructured datasets ensuring high data quality for modeling.
Measure and monitor model performance refining models through experimentation and iteration.
Communicate results effectively through visualizations reports and presentations to support data-driven decision-making.
Contribute to standards and best practices for data science development evaluation and deployment.
Stay up-to-date with emerging techniques tools and approaches in machine learning statistics and AI applying them where appropriate.
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