Senior 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 looking for a Senior Data Scientist with strong expertise in machine learning, advanced analytics, and statistical modelling to design and deliver data-driven solutions that generate measurable business impact.

The role focuses on translating complex business challenges into analytical and AI-driven solutions, developing robust machine learning models, and communicating insights effectively to stakeholders. Working closely with clients, architects, and data engineers, you will leverage modern cloud-based data and AI platforms to deliver scalable analytics, machine learning, and Generative AI capabilities that support strategic decision-making.

Responsibilities

• 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 optimize 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, monitoring, and retraining strategies
• Collaborate with data engineers to access, prepare, and scale datasets from cloud platforms
• Present insights and recommendations through compelling storytelling and data visualisation (MI/BI)
• Contribute to the design of analytics and AI solutions, focusing on delivering business value rather than infrastructure
• Engage with stakeholders and clients during discovery, experimentation, and solution design phases