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Data Scientist

Lisboa

Job description

Collaborate in one of the many data science projects running at our partner.
Support the teams in running tasks like exploratory data analysis, visualization, ML modelling, evaluation and deployment.

We expect the following competencies:

1. Data Acquisition and Preprocessing:
-Identifying relevant data sources, both internal and external
-Extracting, transforming, and loading data from various formats and databases
-Cleaning, normalizing, and handling missing data

2. Exploratory Data Analysis (EDA):
-Conducting statistical analyses to understand data characteristics
-Visualizing data patterns and relationships
-Identifying key features and potential drivers of the target variable

3. Feature Engineering:
-Selecting and creating relevant features for machine learning models
-Transforming raw data into meaningful inputs for the models
-Evaluating the impact of different feature sets on model performance

4. Model Development and Evaluation:
-electing appropriate machine learning algorithms for the problem at hand
-Implementing and training various models (e.g., regression, classification, clustering)
-Tuning model hyperparameters to optimize performance
-Evaluating model performance using appropriate metrics

5. Model Deployment and Monitoring:
-Integrating trained models into production systems
-Monitoring model performance and drift over time
-Updating and retraining models as needed to maintain accuracy

6. Communicating Insights:
-Presenting data-driven insights and recommendations to stakeholders and project management
-Collaborating with cross-functional teams (e.g., business, product, engineering)
-Documenting analysis processes and findings for future reference

7. Continuous Learning and Improvement:
-Staying up-to-date with the latest data science tools, techniques, and best practices
-Identifying opportunities for process improvements and automation
-Mentoring and training junior data scientists

Requirements

We consider relevant a skillset and experience covering:
• Higher education in a STEM related field, with major statistics or mathematics components.
• Five to ten years of experience in Data Science / ML projects.
• Professional experience in end-to-end data science / machine learning initiatives, encompassing data understanding, preparation, modelling and evaluation, and deployment.
• Solid command of data science concepts and principles - especially around EDA, modelling and evaluation - is mandatory.
• Fluency in English, written and spoken, is mandatory. All business discussions will be conducted in English.
• Professional experience with Python and industry standard Python data science / ML frameworks (e.g. scikit, pytorch, keras, opencv, pandas, others) is mandatory.
• Professional experience in data preparation/transformation (e.g. SQL, pandas) is mandatory.
• Professional experience working with a cloud environment is mandatory, AWS highly preferred.
• Experience with LLM, RAG, and Generative AI technologies is highly valued.
• Professional experience with Databricks is highly valued.
• You should have very strong intercultural communication to reconcile different interests
• Hybrid role

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