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Senior Data Scientist

Lisboa

Job description

Key Responsibilities:

Contribute to the design and development of Generative AI solutions, including transformer-based models, embedding, and LLM-based pipelines.
Build and optimize traditional Machine Learning models for various use cases (classification, regression, clustering, etc.).
Explore, pre process, and transform data, applying feature engineering best practices to enhance model performance.
Drive automation and reproducibility of data science pipelines using MLOps tools, ensuring continuous monitoring and maintenance in production.
Communicate analytical results clearly, emphasizing business applicability and impact.
Stay up-to-date with the latest trends and tools in the AI, ML, and cloud ecosystem.

Requirements

Requirements:

Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, Data Science, or related fields.
Proven experience in developing Generative AI solutions, including expertise with transformers, embeddings, and prompt engineering.
Strong proficiency in traditional Machine Learning techniques, including supervised and unsupervised learning.
Fluency in Python and libraries such as NumPy, pandas, scikit-learn, matplotlib, and seaborn.
Experience with PySpark and handling large-scale datasets.
Practical knowledge of MLOps practices, including model versioning, experiment tracking (e.g., MLflow), deployment, and monitoring.
Hands-on experience with cloud environments, specifically:

Azure (e.g., Azure Machine Learning, Data Factory).
GCP (e.g., Vertex AI, BigQuery).
AWS (e.g., SageMaker, S3).

Preferred Qualifications:

Experience with open-source LLMs (e.g., LLaMA, Mistral, Falcon).
Familiarity with frameworks such as LangChain, LangSmith, or LlamaIndex.
Relevant cloud certifications (e.g., Microsoft Azure AI Engineer Associate, Google Cloud Professional Data Engineer, AWS Certified Machine Learning – Specialty).
Ability to work autonomously and contribute to defining team best practices.
Awareness of ethical considerations, algorithmic bias, and data privacy issues.

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