Digital Data Scientist

Hovione is an independent family owned international group of companies. From the beginning, we have set ourselves above and apart through our innovative technological and productive capabilities, reaching a top spot in the world market as an integrated Contract Development and Manufacturing Company dedicated to helping Pharmaceutical Customers bring new and off-patent drugs to market.

Our people are the main asset for our continued success. Reaching over 1950 team members, from 36 different nationalities, that are located across Asia, Europe and North America (Portugal, US, Macao, Ireland, China, Hong Kong, Japan, India, and Switzerland), including 4 production sites.

Working at Hovione means reaching for innovation and excellence in everything we do: for our clients, our partners and, above all, our patients. That is why we are In it for life.

• Partner with stakeholders across functions as a data business partner, identifying high impact use cases and areas for improvement; 
• Build, standardize, and maintain reporting and self-service analytics as part of data centralization (incl. data lake concepts); 
• Preprocess and engineer features from structured and unstructured data, ensuring quality, lineage, and reliability; 
• Mine and analyze data from multiple sources to optimize strategies, operations, and portfolio decisions; 
• Design, develop, and deploy statistical and machine learning models (incl. ensemble approaches) to predict outcomes and detect patterns; 
• Implement models and data products with cross functional teams; set up monitoring, alerting, and lifecycle management; 
• Communicate insights clearly through compelling visualizations and narratives tailored to technical and business audiences; 
• Deliver projects safely, on time, and cost effectively while upholding IT governance, GMP/HSE, and compliance standards (COPs, HBR, SOPs); 
• Proactively propose process optimizations, make timely data-driven decisions, and demonstrate resilience through change. 

• University degree in Statistics, Mathematics, Computer Science, or a similar scientific field; 

• 4–6 years’ experience in data science, including manipulating large datasets, building statistical/ML models and data products

• Fluency in English is a requirement; Portuguese proficiency strongly preferred (ability to engage stakeholders in both languages);

• Strong autonomy to lead analytic solutions from design to deployment;

• Great communication skills and work methodology;

• Knowledge of GMP (even high‑level), data governance, and safe‑by‑design practices; team player with resilience and a long‑term mindset;

• Advanced proficiency in Python and SQL; familiarity with R and object‑oriented scripting is a plus;

• Hands-on experience with cloud data-lake architecture and technology, in particular AWS; familiarity with Python data processing libraries; knowledge of IaC is a plus;

• Hands‑on experience with ML techniques (e.g., clustering, decision trees, neural networks, ensemble methods);

• Strong grounding in statistics (regression, distributions) and experience with LLM optimization and AI assistants;

• Expertise in data visualization and reporting—especially QuickSight —and solid data modeling for BI; proficiency with MS Office.

Hovione is a proud Equal Opportunity Employer

Inclusion and diversity are key to us. At Hovione, that means a work environment where differences are valued, and everyone is treated with fairness and respect. We want all our team members worldwide to feel supported, respected, and have the opportunity to achieve their full potential, regardless of their age, gender, religion, disability, sexual orientation or ethnicity.

Notice to Agencies and Search Firms Representatives

Hovione does not accept unsolicited resumes from agencies or search firms for this job posting. Any resumes submitted to Hovione by a third-party agency or search firm without a valid written and signed search agreement will become the sole property of Hovione. No fees will be paid if a candidate is hired for this position as a result of an unsolicited referral.