Lead Data Scientist

Accelerate your development and exposure to high‑performance data platforms and cloud infrastructure. Join Sedona Digital, a fast‑growing scale‑up with the ambition to be recognised as one of the leading technology companies in Romania. Our global client base needs builders, engineers who enjoy designing and implementing scalable data platforms, have deep expertise in cloud data technologies, and take pride in delivering reliable, well‑governed solutions. At Sedona, we: Obsess about our customers Build robust, scalable technical solutions Create an open, collaborative culture Invest in learning and long‑term careers We are seeking a Lead Data Scientist to lead the design and delivery of enterprise-scale AI, machine learning, and advanced analytics solutions. This role combines technical leadership, solution architecture, and hands-on data science expertise to help clients transform business challenges into measurable outcomes through data and AI. You will define best practices across the Data & AI lifecycle, architect modern Data for AI platforms, design governance and metadata frameworks, and lead multidisciplinary teams delivering scalable analytical and AI solutions. Working closely with business stakeholders, architects, engineers, and client leadership, you will translate complex requirements into practical, high-value solutions leveraging cloud-native AI and data technologies. Responsibilities Leadership of a data science or data & AI team/function Setup and own best practices for a wide range of applied data science and data for AI techniques Act as a consulting data architect for data science or data for AI framework design and implementation Design and leverage data services to solve enterprise analytical or AI requirements including governance metadata e.g. managed RAG, cataloguing, lineage, trust, weighting, usage, history, obsolescence, observability summarisation for risk, compliance, finops & UX. Translate business problems into analytical solutions, identifying opportunities for predictive modelling, optimisation, and data-driven decision-making Design, develop, and deploy machine learning models using techniques such as classification, regression, clustering, and forecasting Leverage LLM analytical capabilities by engineering prompts to securely hosted AI models Apply statistical methods and experimentation techniques (hypothesis testing, A/B testing) to validate models and insights Conduct exploratory data analysis (EDA) to quantify data asset value, identify patterns, trends, and key drivers within large datasets Engineer features and prepare datasets to improve model performance and robustness Evaluate and optimise models using appropriate metrics, cross-validation, and tuning strategies Ensure model explainability and interpretability, communicating results clearly to both technical and non-technical stakeholders Design and implement MLOps practices including model versioning,…