Data Scientist
Turn business questions into reproducible analyses and ML models, using AI-assisted research without compromising statistical rigor or data protection.
Job Description
Mission Nexum is looking for a Data Scientist who can turn ambiguous questions into measurable hypotheses, reliable analyses and models that support real decisions. You will work with technical and business teams from problem framing through evaluation and production handover. What you will do - Translate business and operational questions into hypotheses, target variables, success metrics and analytical plans - Explore, clean and query structured and unstructured data while documenting assumptions and data-quality limitations - Develop features, statistical analyses, baselines and machine-learning models - Design reproducible experiments and evaluate models using appropriate metrics, validation, calibration and error analysis - Explain model behavior, uncertainty, limitations and expected impact to technical and non-technical stakeholders - Work with Machine Learning Engineers to prepare models, features and validation criteria for production - Monitor whether deployed solutions continue to answer the original problem This job posting is addressed to both genders, in compliance with laws 903/77 and 125/91 on equal treatment in the workplace and against gender discrimination. We welcome candidates of all ages and nationalities, in accordance with legislative decrees 215/03 and 216/03. Nexum also encourages applications from people with disabilities, in compliance with current regulations.
How AI supports the role
Use approved generative-AI tools to accelerate data-dictionary creation, profiling suggestions, research synthesis, code and test drafts, and experiment documentation. AI output is treated as a hypothesis rather than evidence: statistical claims, code, citations and conclusions must be verified, and sensitive data is used only in approved environments.
Tool choices vary by project. AI-assisted work remains subject to human review, security controls and documented validation.
What You Bring
Statistics & Machine Learning
Probability, experimental design, supervised and unsupervised methods and robust validation.
Python & SQL
Reproducible data exploration, feature development and model analysis.
Model Evaluation
Metrics, baselines, calibration, explainability and error analysis.
Decision Communication
Ability to connect analytical findings to business and operational decisions.
How We Work
AI-Assisted Engineering
Approved AI tools accelerate analysis, implementation and documentation; people remain accountable for every released artifact.
Production Perspective
The work connects design, delivery, observability and continuous improvement rather than stopping at isolated prototypes.
Evidence & Traceability
Decisions are supported by tests, metrics, provenance and documented trade-offs.
Cross-Functional Delivery
Cloud, data, AI, security and business requirements are connected around real operating constraints.
