Head of Data Science
Role Overview
This is a senior leadership role responsible for defining and executing an enterprise-wide AI and data science strategy within a global asset management organisation. The role will shape how machine learning, advanced analytics, and AI technologies are used to drive investment insight, risk management, client outcomes, and operational efficiency, while ensuring robust governance, ethical standards, and regulatory compliance.
Reporting into senior leadership, this position will lead multidisciplinary teams of data scientists and AI engineers and act as a strategic partner to Investment, Technology, Risk, Compliance, Operations, and Client-facing functions.
Required Qualifications
Education
- Master's degree or Ph.D. in Computer Science, Data Science, Statistics, Engineering, or a closely related quantitative discipline.
- Strong foundations in machine learning, statistics, and algorithmic methods.
Experience
- 10+ years' experience in data science, analytics, or advanced technology roles.
- Minimum of 5 years in leadership or people-management roles.
- Significant experience within financial services is highly desirable, particularly asset management, investment management, or capital markets.
Technical Expertise
- Deep hands-on knowledge of machine learning, statistical modelling, and advanced analytics techniques.
- Proven experience developing, deploying, and maintaining production-grade AI models (e.g. predictive models, NLP, time-series forecasting).
- Strong understanding of model risk management within a regulated environment.
Industry Knowledge
- Solid understanding of the asset management value chain, including:
- Investment products (equities, fixed income, multi-asset)
- Portfolio construction and performance analytics
- Risk management and regulatory reporting
- Client servicing and distribution analytics
- Familiarity with how AI is applied to alpha generation, risk analytics, and compliance monitoring.
Leadership & Communication
- Demonstrated ability to lead high-performing data science and analytics teams.
- Strong stakeholder management skills, with experience presenting to senior executives and committees.
- Ability to translate complex analytical concepts into clear business outcomes and value propositions.
Technical Skills
Programming & Data Science
- Proficiency in Python (pandas, scikit-learn, TensorFlow/PyTorch) and R.
- Strong SQL skills for data querying and analysis.
- Experience with notebooks collaborative data science platforms, and version control (Git).
Machine Learning & MLOps
- Strong understanding of ML techniques including regression, classification, clustering, tree-based models, and neural networks.
- Experience with MLOps practices such as model versioning, automated testing, monitoring, and CI/CD for ML pipelines.
Data Platforms & Cloud
- Experience with relational and non-relational databases, time-series data, and big data frameworks.
- Hands-on use of cloud-based data platforms (e.g. AWS, Azure, or GCP analytics services) and distributed computing environments.
Analytics & Visualisation
- Skilled in BI and visualisation tools such as Tableau, Power BI, or Python-based libraries.
- Ability to communicate insights clearly to non-technical audiences through compelling data storytelling.
AI Ethics & Security
- Knowledge of bias detection, explainability techniques (e.g. SHAP, LIME), and ethical AI practices.
- Understanding of data privacy, anonymisation, encryption, and secure model development when handling sensitive client and investment data.
FAQs
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