Lead Data Scientist

About the role

We are looking for a Lead Data Scientist to act as a strategic advisor, combining deep technical expertise with domain knowledge to solve complex business challenges and drive growth. You will build, mentor, and lead a high‑performing DS&AI team while shaping the organization’s data strategy.

Key responsibilities

• Provide strategic guidance on data‑driven solutions and unlock growth opportunities.
• Unblock team members, ensure delivery under tight timelines without compromising quality.
• Build, mentor, and lead a high‑performing DS&AI team, fostering technical excellence and continuous learning.
• Research, evaluate, and integrate cutting‑edge techniques such as advanced forecasting, personalization, and generative AI into production.
• Define and communicate the technical vision, aligning solutions with company strategy and stakeholder priorities.
• Oversee the full lifecycle of high‑impact DS&AI projects from scoping to production deployment.
• Set technical best practices, review designs, and provide strategic guidance across initiatives.
• Guide experimentation, model validation, and ensure results translate into business KPIs.
• Oversee deployment, monitoring, and scaling of ML and GenAI systems.

Required profile

• MSc in Data Science, AI, Statistics or a related field.
• 5+ years of experience in data science, machine learning or statistical modeling, with at least 1 year in a leadership role.
• Advanced expertise in statistical modeling, experimental design, and specialized ML/DL domains (e.g., recommendation systems, time‑series, NLP, AI agents).
• Proven track record of leading end‑to‑end, high‑impact projects with measurable business value.
• Strong leadership and stakeholder‑management skills, able to define technical direction and align it with strategic priorities.
• Experience scaling ML/DL models in real‑time, low‑latency, streaming environments and leading LLM‑based applications.

Required skills

• Statistical modeling
• Experimental design
• Recommendation systems
• Time‑series analysis
• Natural Language Processing (NLP)
• AI agents
• Machine learning / deep learning (ML/DL)
• Large language models (LLM)
• SQL (complex query and pipeline optimization)
• Advanced forecasting
• Personalization techniques
• Generative AI
• A/B testing and Bayesian approaches
• Model validation and deployment
• Monitoring and scaling of ML systems