Data Scientist / AI Engineer – Analytics & AI Enablement

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🕒 December 17, 2025

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Genesis Digital Solutions

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2018

🤖 Artificial Intelligence

🌐 Web 3

☁️ SaaS

Artificial Intelligence • Web 3 • SaaS

Genesis Digital Solutions is a Lisbon-based digital transformation and IT consulting firm that helps organizations deploy AI-driven solutions, build digital products, and implement Web3 and digital identity technologies. The company offers services in artificial intelligence, data & AI, digital product development, and strategic IT consulting, and provides a SaaS AI platform that accelerates generative AI adoption with no-code, scalable, and customizable integrations. Genesis serves enterprise and government clients across sectors such as healthcare and public services.

📋 Description

• Collaborate closely with Data Engineers and business stakeholders to define and validate curated datasets optimized for analytics and AI use cases. • Specify and document data requirements, feature definitions, and aggregation logic for downstream analytical and machine learning applications. • Validate data completeness, consistency, and statistical soundness of serving-layer datasets. • Provide continuous feedback to data engineering teams on data model usability, performance, and analytical fitness. • Perform exploratory data analysis (EDA) to identify patterns, anomalies, and data quality issues. • Build and test baseline machine learning models and analytical prototypes using curated datasets. • Contribute to the design and implementation of lightweight ML pipelines (training, evaluation, inference) aligned with platform standards. • Ensure features and models are reproducible, versioned, and prepared for future operationalization in production environments.

🎯 Requirements

• 3 to 5 years of experience in Data Science, Applied AI, or Analytics Engineering roles. • Strong proficiency in Python for data analysis and modeling (e.g. pandas, numpy, scikit-learn or equivalent). • Solid SQL skills for working with large-scale analytical datasets. • Experience collaborating with Data Engineers in data lakehouse or data warehouse architectures. • Familiarity with Azure-based analytics and machine learning platforms (e.g. Databricks, Synapse, Azure ML). • Awareness of data quality, governance, and privacy considerations in analytical workflows. • Strong analytical mindset with attention to data validity, assumptions, and statistical robustness. • Ability to translate business and product questions into clear data and feature requirements. • Strong communication skills and a collaborative working style with technical and non-technical stakeholders. • English level B2 or higher (mandatory).

🏖️ Benefits

• A workplace that values innovation and personal growth. • Opportunities to work on high-impact projects. • Remote work model with flexible hours. • Support for professional development, including training and certifications. • Health and life insurance. • 25 days of annual leave.