Data Scientist

• Build, deploy, and iterate on customer churn prediction models
• Identify leading indicators, segment at-risk cohorts, and work with Customer Success on retention playbooks
• Develop propensity models and next-best-action recommendations to help commercial teams prioritise accounts and product conversations
• Design attribution frameworks across marketing channels, sales motions, and product touchpoints
• Build the data products that let leadership see what's actually driving revenue
• Design and analyse A/B tests and quasi-experiments for commercial initiatives
• Take models from notebook to production
• Translate ambiguous commercial questions into well-scoped analytical problems
• Communicate findings clearly to non-technical audiences

Senior Data Scientist

• Support exploratory data analysis across product, marketing, and financial datasets.
• Help collect, clean, and prepare data for modelling and reporting.
• Assist in designing and running experiments to evaluate product features or communications.
• Contribute to machine learning prototypes and decision-support tools.
• Communicate findings clearly through visualisations, notebooks, and written summaries.
• Explore how generative AI tools (Claude, Agents, MCPs) can accelerate data science workflows.

Data Science Intern

• Design, deploy, and scale data science solutions across the organization.
• Support best practices across the team.
• Work closely with Digital and Marketing teams, converting ideas into experiments and models into shipped product features.
• Lead technical discussions and drive engineering best practices across the team.

Lead Data Scientist

• Design, develop and implement Machine Learning models and AI solutions applied to the context of the City of Porto
• Prepare and transform data from multiple sources to feed models and AI systems
• Develop secure, auditable AI systems that comply with ethical and regulatory principles (e.g., the AI Act)
• Develop and integrate solutions based on LLMs (Large Language Models), including RAG architectures, AI agents and task automation
• Deploy models to production in an on-premise environment, ensuring performance, reliability and continuous monitoring
• Conduct exploratory data analysis (EDA) and extract insights to support decisions that impact the community
• Ensure compliance with best practices in privacy, data protection (GDPR) and responsible handling of sensitive information
• Collaborate with Engineering, Product and Projects teams to define technical and functional requirements
• Document architectures, processes, models and results clearly and rigorously
• Evaluate and propose new technologies, frameworks and approaches for the continuous evolution of the AI stack

Data Scientist / AI Engineer

• Act as a Lead Clinical Data Manager
• Coordinate work of other Clinical Data Management staff assigned to the project
• Provide guidance for Data Entry and Data Associates
• Review Study Protocol
• Develop CRF or review sponsor-generated CRFs
• Develop Database
• Prepare, finalize and archive all related DM documentation
• Define and program edit checks
• Develop CRF completion guidelines
• Coordinate validation of database, edit checks and trial and site settings

Clinical Data Manager

• Business Insight & Problem Definition: Partner closely with marketing and product teams to diagnose business challenges and define appropriate data metrics, analyses, and interpretations to drive strategic decisions.
• Statistical Modeling: Define requirements for and lead the creation, monitoring, and iterative improvement of key statistical models used for marketing spend optimization and product growth.
• Narrative & Visualization: Design clear, unambiguous narratives and data visualizations to communicate model outputs and analysis results to broad, non-technical audiences.
• Rapid Prototyping: Independently prototype and test new insights, moving from hypothesis to rigorous validation with minimal mentorship.
• Technical Consultation: Reason effectively about the strengths and weaknesses of different modeling approaches, selecting and iterating on combinations that suit specific marketing contexts.

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Manager, Data Scientist – Engagement & Platform

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• Research new behavioural features and detection algorithms
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