Principal Data Scientist

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, sets technical direction, leads complex initiatives from discovery to production, and turns ambiguous business challenges into scalable, measurable outcomes. The successful candidate will remain hands-on while influencing senior stakeholders, developing talent, strengthening engineering and governance standards, and helping to identify and shape new solution propositions.

Your Role

• Set the technical direction for complex, high-value data science and AI initiatives, aligning solution choices with business strategy, user needs, risk appetite and measurable outcomes.
• Lead end-to-end delivery, from opportunity identification, problem framing and data assessment through experimentation, deployment, adoption, monitoring and continuous improvement.
• Architect and build robust, scalable and maintainable Python-based ML and AI solutions across structured and unstructured data, applying sound software engineering practices.
• Establish rigorous evaluation frameworks, baselines and acceptance criteria, assessing model performance, reliability, fairness, drift, operational readiness and business impact.
• Act as a trusted technical adviser, translating ambiguous business needs into executable roadmaps and clearly communicating options, assumptions, trade-offs, limitations and recommendations.
• Build trusted relationships with senior stakeholders, lead workshops and present strategies, recommendations and outcomes to clients and C-level audiences; and shape new data science and AI propositions, proposals and pre-sales activities.
• Lead technical workstreams and architecture reviews, define delivery plans, manage dependencies and risks, and make pragmatic decisions across quality, speed, cost and operational constraints.
• Mentor data scientists and engineers, provide technical challenge and coaching, and raise standards through reusable patterns, code and design reviews, documentation and knowledge sharing.
• Partner with data engineering, MLOps, platform, product and application engineering teams to create reusable data, feature, training, evaluation and inference capabilities.
• Embed responsible AI, security, privacy, model governance and regulatory requirements throughout the solution lifecycle, while monitoring emerging technologies and recommending practical adoption where they create value

Your Profile

• Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science or Engineering, supported by either an undergraduate degree in Computer Science or prior professional experience as a software engineer.
• 9 to 12 years of extensive hands-on experience delivering data science, machine learning or AI solutions, with a strong track record of technical leadership and measurable enterprise or client impact.
• Deep expertise in applied machine learning, statistics, experimental design and model evaluation, with the judgement to select approaches appropriate to the data, context and operational constraints.
• Advanced proficiency in Python and SQL, with evidence of designing production-quality, testable and maintainable code and contributing to sound technical architecture.
• Proven experience across the full ML lifecycle: data preparation, feature engineering, model development, validation, deployment, monitoring and retraining (embedding MLOps principles).
• Demonstrated ability to lead complex cross-functional initiatives, mentor technical practitioners, raise engineering standards and influence decisions without relying on formal authority.
• Exceptional written and verbal communication skills, with experience presenting complex technical topics, recommendations and business value to clients, senior leaders and non-technical audiences.

What would be a plus

• Experience designing and evaluating generative AI solutions, including large language models, retrieval-augmented generation, agentic workflows, document intelligence, OCR and multimodal or vision-language models, with appropriate guardrails and evaluation methodologies.
• Experience with cloud platforms such as Databricks, experiment tracking, model registries and automated ML delivery pipelines.
• Strong understanding of software engineering and MLOps practices, including APIs, version control, automated testing, CI/CD, containerization, cloud deployment, observability and production support.
• Experience in a regulated industry and practical knowledge of data protection, security, explainability, model risk management and responsible AI controls.
• Experience in consulting, client delivery, solution discovery, proposal development or pre-sales.