Senior Data Scientist

Location: Remote from Spain (Spanish contract)

Join a transformative data and AI platform initiative aimed at modernizing enterprise-scale capabilities and enabling real-time decision-making. This project delivers a comprehensive roadmap covering AI, MLOps, data governance, and platform scalability, supporting a shift towards data-first operations and intelligent automation.

Requirements:

• 4+ years of experience as a Data Scientist, with deep expertise in unsupervised learning, clustering, and advanced exploratory data analysis.
• Strong hands-on experience with SHAP or similar model interpretability techniques.
• Proficiency in Python, Pandas, SQL, Jupyter, and common data manipulation and visualization tools.
• Experience with AWS ecosystem tools like S3, RDS, IAM, and BI solutions such as QuickSight.
• Experience designing and building GenAI or LLM-based workflows, including prompt engineering and integrating APIs.
• Ability to benchmark different LLM solutions and assess their performance for specific summarization and recommendation use cases.
• Skilled in transforming raw outputs into compelling, business-relevant insights for both technical and non-technical audiences.
• Nice to have
• Experience implementing RAG pipelines with vector databases and domain document ingestion.
• Exposure to MLOps workflows and tooling (e.g. MLflow, SageMaker, Airflow, Terraform).
• Prior work on integrating BI platforms with AI/ML pipelines.
• Background in identity verification

Responsibilities:

• Drive the development and evolution of customer clustering models using unsupervised learning to identify patterns in pass rate performance and flag inconsistencies.
• Lead SHAP-based explainability initiatives to uncover the root causes behind verification failures and create dynamic, on-demand explanations.
• Conduct benchmarking of LLM APIs, assessing summarization quality, latency, relevance, and cost to inform GenAI solution design.
• Collaborate on pipeline development to extract, preprocess, and format QuickSight reports for GenAI consumption.
• Build and test proof-of-concept RAG pipelines that enhance LLMs with domain-specific context from historical documents and verification reports.
• Work closely with Delivery Managers to translate complex analytics and model outputs into business-friendly visualizations and narratives.
• Continuously refine clustering methodology by evaluating alternative models, tuning hyperparameters, and expanding criteria.
• Partner with MLOps engineers to ensure seamless integration of data science pipelines into the broader infrastructure, with a focus on automation and scalability.