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. Combining deep expertise in machine learning and AI with a strong software engineering foundation, the role 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.
We see DEI as a business imperative, helping us to attract, grow and inspire a diverse, inclusive, and equitable workforce that better enable us to serve our customers, investors, and communities. Globally, we strive to build balanced teams across all aspects of difference. If you are interested in the role and believe you could contribute, we encourage you to apply even if your experience doesn't perfectly match every qualification.