Data Scientist

Department: Dev & Engineering

Employment Type: Full Time

Location: Portugal

Description

What You Will Be Doing

• Develop, evaluate, and improve machine learning and generative AI solutions that power Intermedia's Digital Agent Platform.
• Apply large language models (LLMs), natural language processing, retrieval, and other advanced AI techniques to improve agent understanding and performance.
• Develop approaches that improve agent reasoning, tool selection, knowledge retrieval, context management, personalization, and task completion.
• Experiment with model, prompt, retrieval, and agent configuration strategies to identify approaches that deliver the best customer and business outcomes.
• Evaluate commercial and open-source models and recommend appropriate approaches based on quality, latency, scalability, and cost.

• Design, develop, and optimize Retrieval-Augmented Generation (RAG) solutions that ground digital agents in relevant enterprise and customer information.
• Develop and evaluate embeddings, retrieval strategies, ranking approaches, semantic search, and other knowledge-retrieval techniques.
• Build and refine end-to-end pipelines that combine LLMs with retrieval systems, enterprise knowledge sources, and agent workflows.
• Develop approaches to improve the relevance, accuracy, and consistency of AI-generated responses.
• Identify and mitigate issues such as hallucinations, poor retrieval, inappropriate responses, and other failure modes in generative AI applications.

• Develop rigorous evaluation frameworks for measuring digital agent quality, including accuracy, relevance, task completion, reliability, safety, and customer experience.
• Design offline and online experiments to compare models, prompts, retrieval strategies, agent configurations, and other AI approaches.
• Apply statistical analysis, hypothesis testing, segmentation, and other quantitative methods to evaluate AI performance and identify opportunities for improvement.
• Define appropriate metrics and benchmarks that connect model and agent performance to customer and business outcomes.
• Analyze production behavior and feedback to identify patterns, failure modes, and opportunities to continuously improve digital agents.
• Ensure data and model quality through comprehensive testing, validation, and performance evaluation.

• Gather, preprocess, analyze, and model large volumes of structured and unstructured data from multiple sources.
• Use Python, SQL, Spark, and other relevant technologies to build scalable analytical and machine learning solutions.
• Develop features, datasets, and analytical approaches that support model development, experimentation, and evaluation.
• Partner with data and engineering teams to build and maintain reliable pipelines for model training, evaluation, and production use.
• Contribute to scalable ML/AI pipelines that support experimentation, deployment, monitoring, and continuous improvement.
• Ensure solutions are reproducible, maintainable, and designed to operate effectively at production scale.

• Partner with Product, AI/ML Engineering, Software Engineering, and other Data Science team members to translate customer and business problems into effective AI solutions.
• Communicate analytical findings, model performance, tradeoffs, and recommendations clearly to technical and non-technical stakeholders.
• Participate in technical and design reviews and help establish strong practices for AI experimentation, evaluation, and model development.
• Mentor and support less experienced data scientists and contribute to knowledge sharing across the team.

What You Will Bring to the Role

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Machine Learning, Analytics, or a related quantitative field; Master's degree preferred.
• 4+ years of professional experience in data science, machine learning, applied AI, or a related analytical field.
• Strong experience developing and applying supervised and unsupervised machine learning models to real-world problems.
• Hands-on experience with generative AI, large language models, NLP, or other modern AI technologies.
• Strong programming skills in Python and experience with SQL and large-scale data processing technologies.
• Experience with modern machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies.
• Strong understanding of statistical analysis, experimentation, hypothesis testing, model evaluation, and performance measurement.
• Experience working with large volumes of structured and unstructured data.
• Demonstrated ability to independently frame ambiguous problems, develop analytical approaches, evaluate alternatives, and deliver actionable solutions.
• Experience taking machine learning or AI solutions beyond experimentation and contributing to their deployment and operation in production environments.
• Understanding of model quality, data quality, bias, reliability, and other considerations associated with production AI systems.
• Strong problem-solving and analytical skills with the ability to connect technical results to customer and business outcomes.
• Strong written and verbal communication skills with the ability to explain complex analytical and AI concepts to technical and non-technical audiences.
• Ability to collaborate effectively across Data Science, AI/ML Engineering, Software Engineering, Product, and business teams.

Diversity, Inclusion, and Equal Opportunity