Senior Data Scientist – AI Strategy & Advanced Analytics

We are: Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact. With the right people and the right ideas, there’s no limit to what we can achieve Are you a fit? Sounds awesome, right? Now, let’s make sure you’re a good fit for the role: Must-have Skills: Advanced Modeling: Leads end-to-end design of high-complexity models, including deep learning, advanced forecasting, optimization, and NLP. Data Science Strategy: Partners with senior leadership to define data science strategy and roadmaps, guiding the analytical direction of the organization. Feature Engineering & Experimentation: Architects feature engineering frameworks, experimentation standards, and modeling conventions. Applies strong knowledge of experimentation and causal inference. Azure & Databricks: Oversees deployment and integration of models using Spark and Databricks. Strong experience deploying models in Azure Databricks using MLflow. Model Monitoring: Skilled in monitoring model performance and detecting model drift in production environments. Technical Leadership: Mentors and technically guides L1 and L2 Data Scientists. Reviews and standardizes best practices for code, modeling, documentation, and validation. ML + BI Integration: Applies strong critical thinking to integrate machine learning outputs with business intelligence tools (Power BI preferred) to drive business impact. Executive Communication: Communicates analytical direction and insights to C-level and VP-level stakeholders. Technical English: Proficiency in reading, writing, and presenting in English within technical and executive contexts. Nice-to-have: AI Genie (Databricks): Experience working with AI Genie in Databricks environments. Thought Leadership: Publications, open-source contributions, training, or conference participation. Data & AI Governance: Exposure to data and AI governance and enterprise data strategy. AI Tooling Proficiency : Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows. Leadership experience in ML platform or DevOps teams. Experience with feature stores and feature engineering. AutoML is a plus, H2O is a plus. What we offer: A High-Impact Environment Commitment to Professional Development Flexible and Collaborative Culture Global Opportunities Vibrant Community Total Rewards *Specific benefits are determined by the employment type and location. Find out more about our culture here .