Lead Data Scientist

Twoday is one of the leading digital transformation partners in Northern Europe with a global presence. With approximately 3,000 technologists, we collaborate with the most admired private and public organizations to deliver cutting-edge digital solutions. Our deep industry expertise includes Data & AI, software development, digital experiences, and business applications. Operating across the Nordics and Lithuania, our team serves over 8,000 customers and supporting their digital transformation journeys.  We are looking for an experienced Lead Data Scientist to join our AI team! Most organizations have no shortage of ideas for what to model. Far fewer can turn a vaguely stated business problem into a well-posed prediction task, pick the algorithmic approach that fits it, and defend the result when someone asks how the number was produced. That gap is where you come in. You will lead the analytical work that turns a customer's business problem into a model that holds up, and then into something they actually use, alongside data engineers, AI/ML engineers, cloud architects, and software developers on some of the largest Data & AI teams in the Nordics. You will set the analytical direction on our most demanding engagements, and you will grow the people who deliver them. What are we looking for? You are a scientist and a builder. Given a business problem stated in a customer's own words, you can work out what should be predicted, at what granularity, against which target, with what horizon, and what a good result would even look like. You are rigorous about how you validate a result, and you have seen enough projects to have opinions about which problems are worth modelling at all and which should be solved another way. We expect you to bring: Several years of hands-on data science experience, including a number of models you have taken past the prototype stage and to production. Proven strength in translating a business objective into a formal AI/ML problem: defining the target variable and unit of analysis, agreeing what success means with the people who will use the model, and saying early when the data will not support the question. Deep predictive modelling craft: classification, regression, time series and generative AI, and the ability to set the modelling standard for others. Judgement about algorithmic choice: when a well-specified baseline or classical statistical model is enough, when a heavier method earns its cost, and when the problem is not a prediction problem at all. Solid applied statistics: honest validation, uncertainty, and the ability to explain what a result does and does not support. Strong software fundamentals to write code others can build on. Experience on Azure: Azure Machine Learning, Databricks, or Microsoft Fabric (Experience from AWS or GCP transfers, and we will not hold it against you.) Experience applying generative AI where it genuinely fits, including RAG architectures and the evaluation…