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Data Scientist II - Job Opportunity at LexisNexis Risk Solutions

Dublin, Ireland
Full-time
Mid-level
Posted: June 17, 2025
On-site
EUR 55,000 - 75,000 annually. This estimate reflects the mid-level positioning of the role in Dublin's competitive data science market, considering the specialized insurance domain expertise required and LexisNexis's position as an established technology provider. The salary range accounts for Dublin's higher cost of living compared to other Irish cities and the premium placed on candidates with both technical skills and industry knowledge.

Benefits

Access to comprehensive insurance industry datasets and advanced analytics platforms that provide significant professional development opportunities in specialized domain expertise
Opportunity to work with cutting-edge statistical modeling technologies and methodologies that enhance technical skill portfolio and market competitiveness
Exposure to global data science initiatives and cross-functional collaboration that builds strategic business acumen and leadership capabilities
Professional development through research and innovation projects that contribute to thought leadership and industry recognition

Key Responsibilities

Architect and deploy sophisticated statistical models for insurance pricing, underwriting, and fraud detection that directly impact customer acquisition strategies and revenue optimization across multiple market segments
Transform complex analytical findings into compelling business narratives that drive strategic decision-making and demonstrate quantifiable value propositions to executive stakeholders and enterprise clients
Establish and champion modeling excellence standards while implementing process improvements that enhance operational efficiency and reduce time-to-market for analytical solutions
Serve as the primary technical advisor and subject matter expert to cross-functional teams, enabling data-driven product development and sales enablement initiatives
Lead innovation initiatives by identifying and evaluating emerging technologies that position the organization at the forefront of insurance analytics and risk assessment methodologies
Contribute to enterprise-wide data science strategy formulation and execution that aligns with business objectives and drives competitive advantage in the insurance technology market

Requirements

Education

Master's Degree in computer science, mathematics, statistics or quantitative methods (or equivalent experience)

Experience

Demonstrable experience or knowledge of applied modelling and analytics experience in applicable industry

Required Skills

Good understanding of statistical methods applied to data analysis User experience of R, Python, SAS, SPSS or equivalent analytic software Understanding of various statistical methodologies including linear regression, logistic regression, and other advanced analytic techniques Good written communication skills, including the ability to describe statistical results to non-statistical audiences Experience processing large data sets and matching/merging multiple data sets
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Sauge AI Market Intelligence

Industry Trends

The insurance technology sector is experiencing rapid digital transformation with increased adoption of artificial intelligence and machine learning for risk assessment, creating substantial demand for data scientists who can bridge traditional actuarial science with modern analytics methodologies. Insurance companies are investing heavily in predictive modeling capabilities to improve underwriting accuracy and reduce claims costs. Regulatory compliance requirements in the insurance industry are becoming more stringent, particularly around algorithmic fairness and model explainability, driving the need for data scientists who understand both technical implementation and regulatory frameworks. This trend is particularly pronounced in European markets following GDPR and emerging AI regulation. The convergence of traditional insurance with insurtech startups is creating a competitive landscape where advanced analytics capabilities determine market positioning, leading to increased investment in data science talent and infrastructure across established players like LexisNexis Risk Solutions.

Role Significance

Based on the description of a 'small team' structure, this role likely involves working within a core data science team of 3-5 members, with regular collaboration across Product, Sales, and Technology teams totaling 15-25 cross-functional partners. The consultant-style responsibilities suggest significant autonomy and individual accountability.
This Data Scientist II position represents a mid-level role with significant individual contributor responsibilities and growing influence on strategic initiatives. The role combines hands-on technical work with business consultation, indicating a progression beyond entry-level positions toward technical leadership. The emphasis on shaping data science strategy suggests preparation for senior or principal data scientist roles.

Key Projects

Development and deployment of insurance pricing models that directly impact product profitability and market competitiveness Fraud detection system enhancement projects that combine traditional rule-based approaches with machine learning methodologies Customer risk assessment model optimization initiatives that improve underwriting accuracy and speed Cross-functional analytics projects supporting sales enablement and product development initiatives

Success Factors

Ability to translate complex statistical concepts into actionable business insights that drive revenue growth and operational efficiency, requiring both technical depth and strong communication skills to influence non-technical stakeholders effectively. Proficiency in balancing multiple project types from routine analytics to innovative research initiatives, demonstrating project management capabilities and the ability to prioritize competing demands in a fast-paced business environment. Development of deep insurance industry expertise combined with technical excellence, positioning oneself as a domain expert who can anticipate market needs and regulatory requirements while implementing cutting-edge analytical solutions. Building strong cross-functional relationships and establishing credibility as a trusted advisor to Product, Sales, and Technology teams, enabling successful collaboration and adoption of data-driven recommendations across the organization.

Market Demand

High demand. The intersection of data science expertise with insurance domain knowledge represents a particularly sought-after skill combination in the current market. Dublin's growing position as a European technology hub, combined with the specialized nature of insurance analytics, creates strong competition for qualified candidates.

Important Skills

Critical Skills

Statistical modeling expertise is absolutely essential as this forms the core of the role's value proposition. The ability to build, validate, and optimize predictive models directly impacts business outcomes and customer satisfaction. Advanced understanding of regression techniques, machine learning algorithms, and model validation methods distinguishes successful candidates in this competitive field. Programming proficiency in R, Python, or equivalent platforms is critical for model implementation and data processing at scale. The insurance industry's large datasets and complex regulatory requirements demand technical skills that can handle enterprise-level data processing and integration challenges effectively. Communication skills for translating technical results to business audiences represent a critical differentiator in this role. The ability to influence strategic decisions through clear presentation of analytical insights determines the ultimate impact and career advancement potential for data scientists in business-facing roles.

Beneficial Skills

Insurance industry knowledge and regulatory familiarity provide significant advantages in understanding context and constraints for model development. Professionals with actuarial background or previous insurance experience can accelerate their impact and career progression. Cloud computing and big data technologies including AWS, Azure, or Google Cloud platforms are increasingly valuable as insurance companies modernize their technical infrastructure and adopt scalable analytics solutions. Project management and agile methodology experience supports success in cross-functional collaboration and helps data scientists effectively manage multiple concurrent initiatives while meeting business deadlines and quality standards.

Unique Aspects

The combination of insurance domain expertise with advanced analytics creates a specialized career path that offers significant market differentiation and long-term value in the growing insurtech sector.
Direct involvement in shaping global data science strategy provides unusual exposure to executive-level decision-making and strategic planning for mid-level professionals.
The role's balance between customer-facing consultation and internal technical development offers diverse experience that prepares candidates for multiple career trajectories.
Access to LexisNexis's extensive proprietary datasets and established customer relationships provides unique opportunities to work on large-scale, high-impact projects that directly influence industry practices.

Career Growth

Typical progression to senior technical roles occurs within 2-3 years with demonstrated impact on business outcomes and technical leadership. Management track opportunities typically emerge within 3-5 years, depending on organizational growth and individual performance in cross-functional collaboration and strategic initiatives.

Potential Next Roles

Senior Data Scientist or Principal Data Scientist roles within LexisNexis Risk Solutions or similar insurance technology companies, focusing on advanced model development and team leadership responsibilities Data Science Manager or Analytics Manager positions that combine technical expertise with people management and strategic planning responsibilities Product Manager roles in insurance technology companies, leveraging deep analytical background to drive product strategy and development Risk Analytics Consultant or Solutions Architect roles that involve client-facing responsibilities and solution design for enterprise customers

Company Overview

LexisNexis Risk Solutions

LexisNexis Risk Solutions operates as a leading provider of risk assessment and decision-making tools in the insurance industry, combining extensive data assets with advanced analytics capabilities. The company serves as an essential infrastructure provider for insurance companies, offering solutions that span the entire insurance lifecycle from underwriting to claims processing. Their position in the market is built on decades of data accumulation and regulatory expertise.

LexisNexis Risk Solutions holds a dominant position in the insurance data and analytics market, competing with companies like Verisk Analytics and SAS Institute. Their established customer relationships and comprehensive data assets create significant competitive advantages and market stability. The company's scale enables substantial investment in research and development while maintaining strong financial performance.
The Dublin location represents LexisNexis's strategic European operations hub, providing access to EU markets while leveraging Ireland's favorable business environment and skilled workforce. This positioning allows the company to serve European insurance markets effectively while maintaining compliance with regional regulatory requirements. Dublin's growing technology ecosystem provides access to top-tier talent and collaborative opportunities.
The emphasis on small team collaboration and cross-functional partnership suggests a culture that values individual contribution and direct impact on business outcomes. The focus on research and innovation indicates an environment that encourages experimentation and professional development. The global data science group structure suggests opportunities for knowledge sharing and career advancement across international operations.
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