SENIOR DATA SCIENTIST (HYBRID)
Portuguese company hires for hybrid work
Location: Lisbon / Portugal
Work model: 2/3 times per week in Client (Lisboa)
Language Requirements: English, Portuguese (OPTIONAL), Spanish (OPTIONAL)
Seniority: 9+ years of experience
Sector: Energy
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.
We are seeking a highly skilled Data Scientist to design and implement advanced analytical models and intelligent applications that seamlessly integrate Generative AI into real-world workflows.
This role involves:
- Building and maintaining robust machine learning pipelines
- Deploying models into production environments
- Contributing to strategic initiatives within a collaborative, global team setting
Key Deliverables
- Development of highly complex analytical models (e.g., combinatorial models, deep learning on structured and unstructured data at rest)
- Implementation of code (R, Python) and/or proficient use of cloud PaaS services (preferably Azure) alongside advanced analytics frameworks (TensorFlow, Keras, Caffe, PyTorch)
- Delivery of solutions such as:
Generative AI models or applications (LLMs, diffusion models) integrated into business workflows (e.g., text generation, summarization, contextual classification, image creation, autonomous agents, or intelligent copilots)
End-to-end ML pipelines, including preprocessing, fine-tuning of base models (e.g., LLaMA, Mistral, T5), evaluation with domain-specific metrics (BLEU, ROUGE, perplexity), and controlled deployment (e.g., via API), demonstrating best practices in machine learning engineering
- Generative AI models or applications (LLMs, diffusion models) integrated into business workflows (e.g., text generation, summarization, contextual classification, image creation, autonomous agents, or intelligent copilots)
- End-to-end ML pipelines, including preprocessing, fine-tuning of base models (e.g., LLaMA, Mistral, T5), evaluation with domain-specific metrics (BLEU, ROUGE, perplexity), and controlled deployment (e.g., via API), demonstrating best practices in machine learning engineering
Must-Have Qualifications / Must be clearly listed on your CV
- Bachelor’s degree in Software Engineering, Computer Engineering, or a related field
- Strong written and verbal communication skills in English
- Ability to collaborate effectively in global, cross-functional teams
- Strong analytical thinking and problem-solving mindset
- Proven track record of delivering high-quality results
- Hands-on experience with relational and NoSQL databases
- Practical experience developing machine learning projects (Python, R, or SAS), including production deployment and lifecycle management
- Proficiency with machine learning frameworks such as PyTorch and TensorFlow
Nice-to-Have Qualifications
- Knowledge of the Energy & Utilities market and GDPR compliance
- Familiarity with Agile methodologies, JIRA, and Confluence
- Experience working on strategic, large-scale projects
- Additional language skills: Portuguese (valued), Spanish (valued)
- Experience implementing interactive data visualization solutions to present complex results
- Prior experience developing AI-driven analytical solutions (highly valued)
Top Skills
- Exceptional written and verbal communication
- Ability to thrive in global, cross-functional teams
- Critical thinking and problem-solving focus
- Strong commitment to quality and precision
- Advanced experience with relational and NoSQL databases
- Expertise in developing and deploying ML projects with Python, R, or SAS
- Solid knowledge of ML frameworks: PyTorch, TensorFlow
- Proven ability to design and implement advanced analytical models (e.g., deep learning, combinatorial optimization)
- Experience integrating Generative AI (LLMs, diffusion models) into production-grade workflows
- Skilled in preprocessing, fine-tuning (e.g., LLaMA, Mistral, T5), and evaluating models using metrics like BLEU, ROUGE, and perplexity
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