Principal Data Scientist

About the role

The Principal Data Scientist is a senior technical leader who shapes and delivers high-impact data science and AI solutions for clients and internal business areas. The role combines deep expertise in machine learning and AI with a strong software engineering foundation, setting technical direction, leading complex initiatives from discovery to production and turning ambiguous business challenges into measurable outcomes.

Key responsibilities

• Set technical direction for complex, high-value data science and AI initiatives, aligned with business strategy, user needs, risk appetite and measurable outcomes.
• Lead end-to-end delivery, from opportunity identification and data assessment through experimentation, deployment, adoption, monitoring and continuous improvement.
• Architect and build robust, scalable Python-based ML and AI solutions across structured and unstructured data, using sound software engineering practices.
• Establish evaluation frameworks, baselines and acceptance criteria covering model performance, reliability, fairness, drift, operational readiness and business impact.
• Act as a trusted technical adviser, translating business needs into roadmaps and presenting options, trade-offs and recommendations to clients and C-level audiences.
• Shape new data science and AI propositions and pre-sales activities, and lead technical workstreams and architecture reviews.
• Mentor data scientists and engineers, and partner with data engineering, MLOps, platform, product and application engineering teams to build reusable data, feature, training, evaluation and inference capabilities.
• Embed embedded AI, security, privacy, model governance and regulatory requirements throughout the solution lifecycle.

Required profile

• Master's degree in Computer Science, AI, Machine Learning, Data Science or Engineering, supported by an undergraduate degree in Computer Science or prior software engineering experience.
• 9 to 12 years of hands-on experience delivering data science, ML or AI solutions, with a track record of technical leadership and enterprise or client impact.
• Proven experience across the full ML lifecycle, including deployment, monitoring and retraining with MLOps principles.
• Demonstrated ability to lead cross-functional initiatives, mentor practitioners and influence decisions without formal authority.
• Exceptional written and verbal communication skills, presenting complex technical topics to clients and senior leaders.

Required skills

• Deep expertise in applied machine learning, statistics, experimental design and model evaluation.
• Advanced Python and SQL, with production-quality, testable code and sound technical architecture.
• Full ML lifecycle: data preparation, feature engineering, model development, validation, deployment, monitoring and retraining.

What would be a plus: experience with generative AI (LLMs, RAG, agentic workflows, OCR, vision-language models), cloud platforms such as Databricks, MLOps practices (CI/CD, containerization, observability), regulated industries and data protection, and consulting or pre-sales experience. Candidates are encouraged to apply even if their experience does not match every requirement.