Data Scientist

Lead Data Scientist

Marketing & Distribution Analytics (Financial Services)

Sector: Financial Services (Asset Management, Life Insurance & Retirement)
Location: Ireland (Hybrid – 3 days on-site per week)
Contract: Permanent, Full-Time

The Opportunity

A global technology and professional services organisation with a long-established Financial Services practice is expanding its advanced analytics capability in Ireland. They are appointing a Lead Data Scientist to anchor and scale their Marketing & Distribution Analytics function across Asset Management, Life Insurance, and Retirement solutions.

This is a technical leadership role, not a purely individual contributor position. The successful candidate will define how predictive analytics, segmentation, and propensity modelling are designed, governed, and operationalised across the business. You will also mentor a small but growing team of data scientists and act as a trusted partner to senior commercial leaders.

The focus is clear: analytics that drives commercial action, not reporting for its own sake.

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

4. Team Leadership

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

Essential Experience

• 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

Desirable Experience

• 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)