Data Scientist

p#WEAREALTIA /p pbALTIA /bis a multidisciplinary team focused on delivering advanced data-driven solutions that generate measurable impact and enable smarter decision-making. Working across international environments, we combine analytics, artificial intelligence, and business expertise to transform data into strategic value. Our mission is to help organizations evolve through innovation, scalable data platforms, and high-quality analytical solutions, fostering continuous learning, collaboration, and technological excellence. /p pWe are looking for a bData Scientist /b with solid expertise in agricultural data governance, machine learning, statistical modeling, data quality, and AI compliance to join our team in international digital transformation initiatives supporting data-driven policy design and analytical ecosystems. /p pThe role involves developing, validating, and deploying end-to-end machine learning algorithms, statistical models, and predictive scripts tailored to agricultural datasets. The specialist will design and execute rigorous experimental frameworks (such as A/B testing, cross-validation, and statistical significance testing), as well as build scalable data integration, transformation, and automated validation pipelines. Additionally, the candidate will focus on implementing responsible AI governance frameworks (aligned with transparency, explainability, fairness, and the EU AI Act), model risk management, drift/bias monitoring, data policy enforcement, and user acceptance testing (UAT). /p pIn close collaboration with technical teams, policy leads, data providers, and data users, the specialist will perform data requirement analysis, model documentation (e.g., model cards), API-based data integration, and user support workflows. The position requires strong proficiency in Python, SQL, Power BI, or R, along with practical experience in distributed data architectures (data lakes, lakehouses, NoSQL) and automated data quality checks in the agricultural sector. /p h3WHAT WE HAVE FOR YOU /h3 ul liRemote work model (far-site delivery with occasional presence in Brussels if required). /li liFlexible

working hours
. /li liLong-term stable position with professional growth opportunities. /li liContinuous learning and upskilling in cutting-edge AI/ML methodologies, data science, and emerging regulatory frameworks. /li liIntegration into an experienced, highly skilled, and collaborative international team. /li liParticipation in high-impact international digital and data governance projects. /li liInclusive and innovation-driven work environment. /li /ul h3REQUIREMENTS /h3 pEducational level

Experience:
/p ul liEQF Level 7 (Master's degree or 5 years of higher education) + minimum 5 years of general IT experience, OR /li liEQF Level 6 (Bachelor's degree or 3 years of higher education) + minimum 7 years of general IT experience. /li /ul pGeneral Domain

Experience:
/p ul liMinimum 7 years of specific experience in data analysis or data science. /li liMinimum 2 years of experience in data management and visualization tools applied to the agricultural field. /li /ul pLanguages: /p ul liMinimum B2 level in English (spoken and written). /li /ul pTECHNICAL FUNCTIONAL

EXPERIENCE:
/p ul liEnd-to-end Machine Learning Analytics: Development and deployment of ML algorithms, statistical modeling, forecasting, recommendation systems, anomaly detection, and trend analysis. /li liExperimental Design Testing: Experiment design (e.g., A/B testing), cross-validation, statistical significance testing, and User Acceptance Testing (UAT) for data IT solutions. /li liData Lifecycle Governance: Data collection, ETL/ELT transformation, validation, automated quality checks, data policy enforcement, and publishing workflows. /li liResponsible AI Regulatory Frameworks: AI governance principles (transparency, explainability, fairness, accountability), model risk management, drift and bias monitoring, and EU AI Act compliance (model cards). /li liData Architecture APIs: Data extraction via REST APIs, database environment comparisons, and storage architectures across SQL, NoSQL, MongoDB, Hadoop, Data Lakes, and Lakehouses. /li liUser Stakeholder Support: Creating guidance documents, templates, tools, and supporting helpdesk inquiries for data users and providers. /li /ul pTECHNOLOGIES

SKILLS:
/p ul liProgramming Tools: Python, SQL, R, R Studio, SQL Developer, Excel. /li liBI Visualization: Power BI, interactive dashboards, KPI tracking, and analytical reporting. /li liStorage Querying: Relational databases (SQL), NoSQL, MongoDB, Hadoop, Data Lakes, Lakehouses, distributed stores. /li liData Integration: APIs, ETL/ELT processes, data pipeline execution, and schema validation. /li liSoft

Skills:
Strong problem-framing capabilities, ability to communicate complex model insights clearly, results-oriented mindset, and ability to work autonomously and in multilingual teams. /li /ul pNICE TO HAVE: /p ul liWorking know