Sector: Financial Services (Asset Management, Life Insurance & Retirement)
Location: Ireland (Hybrid 1– 3 days on-site per week)
Contract: Permanent, Full-Time
Role Purpose
The Lead Data Scientist will own end-to-end delivery of marketing and distribution analytics within the Financial Services practice. The emphasis is on measurable commercial impact, including:
Improving conversion through advanced customer segmentation
Driving distribution decisions via propensity and predictive models
Quantifying customer lifetime value across products and channels
Turning insight into clear commercial trade-offs and decisions
The role operates at the intersection of data science, commercial strategy, and regulated model governance, requiring both technical depth and the ability to communicate clearly in a Financial Services environment subject to model-risk scrutiny.
Key Responsibilities
1. Analytical Leadership
Lead the design, development, and deployment of advanced data science models across segmentation, propensity scoring, predictive analytics, and lifetime value modelling
Translate ambiguous commercial questions into structured, solvable analytical problems
Own the Marketing & Distribution Analytics roadmap, prioritising work based on commercial impact and stakeholder needs
Ensure outputs are decision-oriented rather than purely descriptive analytics
2. Technical Delivery
Build and deploy production-grade models using Python and SQL
Operate across cloud environments including Amazon Web Services (AWS) and Microsoft Azure (Azure)
Develop clear, decision-focused reporting layers using Microsoft Power BI, ensuring insight is accessible to non-technical stakeholders
Integrate intent and signal-based data sources (e.g. 6sense, Bombora, or equivalents) into marketing and distribution models
Establish and enforce standards for model documentation, reproducibility, validation, and version control
3. Stakeholder Partnership
Partner with senior leaders across Marketing, Distribution, Product, and Commercial functions
Communicate complex analytical outcomes in a clear, business-focused manner for executive audiences
Provide credible challenge to assumptions and shape commercial strategy through evidence-based insight
Act as the primary technical voice in planning and decision forums
Mentor and develop a small team of data scientists
Set technical standards and ensure consistency in modelling approaches and outputs
Review analytical work for technical robustness, commercial alignment, and governance compliance
Contribute to hiring, onboarding, and capability development within the analytics function
5. Governance & Controls
Ensure all models comply with internal model risk management, audit, and regulatory requirements
Maintain alignment with data privacy, governance, and ethical AI standards
Support model validation processes in collaboration with risk and compliance teams
Ensure transparency and explainability of all deployed models in line with Financial Services expectations
8–10 years’ experience in Data Science, Advanced Analytics, or Marketing Analytics
Significant exposure to Financial Services environments
Proven track record of delivering production-grade ML/AI models that have driven measurable commercial outcomes
Strong hands-on expertise in Python and SQL across full model lifecycle
Experience deploying solutions across AWS and Azure
Practical use of Power BI for decision support (not just visual reporting)
Familiarity with intent/signal platforms such as 6sense, Bombora, or equivalents
Experience mentoring or leading junior data scientists and establishing technical standards
Direct experience in Asset Management, Life Insurance, or Retirement sectors
Exposure to model risk governance, validation, and regulatory oversight frameworks
Experience working with MarTech, CRM, CDP, or distribution platform data ecosystems
Postgraduate qualification in a quantitative discipline (Statistics, Computer Science, Mathematics, Engineering, or related field)
Commercial judgement: Ability to translate analytical outputs into business decisions with measurable impact
Technical credibility: Deep expertise across modern data science tools and methods, with authority to set technical standards
Communication: Clear articulation of complex insights to senior, non-technical stakeholders
Leadership: Commitment to developing others and shaping a growing analytics function
Pragmatism: Ability to balance advanced modelling with regulatory, operational, and commercial constraints
Additional Information
This is a permanent role based in Ireland with a hybrid working model (1-3 days on-site per week). The organisation offers strong career progression opportunities within a globally recognised business and a Financial Services practice that continues to invest heavily in its data and analytics capability.
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