Senior Data Analyst

Introduction

Through our client-facing brands Metropolitan and Momentum, with Multiply (wellness and rewards programme), and our other specialist brands, including Guardrisk and Eris Property Group, the group enables business and people from all walks of life to achieve their financial goals and life aspirations. We help people grow their savings, protect what matters to them and invest for the future. We help companies and organisations care for and reward their employees and members. Through our own network of advisers or via independent brokers and utilising new platforms Momentum Metropolitan provides practical financial solutions for people, communities, and businesses.

Disclaimer

As an applicant, please verify the legitimacy of this job advert on our company career page.

Role Purpose

Apply statistical and predictive modelling techniques to extract insights, identify trends, and support data-driven decision making, with potential to extend analysis into artificial intelligence (AI) and machine learning (ML) applications. The Senior Data Analyst will serve as the technical lead for the data analytics function, ensuring analytical accuracy, methodological consistency and data integrity across all outputs.

The role focuses on advanced analytics, statistical modelling, and data visualization to provide actionable insights to inform business strategies. This role collaborates closely with the BI manager to validate models, verify results and deliver actionable insights that inform business strategies and reporting.

Requirements

Qualification(s)

• B Degree in Data Science, Computer Science, Business Analytics, Statistics, Engineering or equivalent quantitative qualification.

• Postgraduate qualifications or master's degree in quantitative discipline are advantageous.

• Certification in data analysis, machine learning or visualization tools is an advantageous.

Knowledge

• Advanced statistical theory (probability, inferential statistics, experimental design).

• Strong understanding of predictive modelling, data science and machine learning concepts.

• Understanding financial and operational performance metrics and their analytical application.

• Familiarity with data environments, data warehouse and enterprise data structures.

• Knowledge of business intelligence and data analytics systems, methodologic sand processes.

• Knowledge of financial service industry.

• Working knowledge of data governance and data quality standards.

Skills

• Proficient in Python, R, or statistical tools like SAS / STATA for statistical modelling and predictive analytics (pandas, scikit-learn, tidyverse, etc.).

• Strong SQL for data extraction, transformation and validation.

• Expertise in Power BI or Tableau for visualisation and storytelling.

• Excellent analytical thinking and problem-solving.

• Ability to translate data into actionable business insights for non-technical audiences.

• Strong communication and presentation skills.

• High attention to detail and commitment to data integrity.

Experience

• Minimum of 7 years of relevant experience in data analysis, statistical modelling, or applied analytics, preferably in the Financial Services / Healthcare industry.

• Proficiency in statistical analysis tools (e.g., R, Python), data visualisation tools (e.g., Power BI, Tableau), and SQL.

• Proven track record in developing, validating and operationalising predictive models.

• Experience working with large-scale enterprise datasets and BI platforms.

• Demonstrated ability to verify, review and quality check analytical work completed by junior team members.

• Exposure to cloud-based analytics environments is advantageous (Azure ML, Databricks, or Azure synapse analytics).

Duties & Responsibilities

INTERNAL PROCESS:

• Technical Leadership: Provide analytical direction and quality assurance for the team of data analysts by reviewing methodologies, verifying statistical results, ensuring accuracy and consistency in all analytical outputs.
• Data Analysis: Collect, process, and analyse large datasets from multiple sources (claims, policies, operational or customer data) to support data-driven decision making.
• Statistical Modelling: Develop and apply statistical and predictive models (regression, clustering, hypothesis testing, and forecasting) using Python, R or statistical tools like SAS / STATA.
• Model Deployment: Operationalise and deploy analytical and predictive models into production environments, dashboards or APIs in collaboration with BI Developers and IT teams, ensuring scalability, monitoring and version control.
• Data Visualisation: Translate analytical results into clear and actionable insights and present them using data visualisation tools (Power BI, Tableau).
• Reporting: Prepare comprehensive analytical reports and executive dashboards that summarise key findings, trends and recommendations for business stakeholders.
• Data Quality Assurance: Conduct validation and verification of datasets to ensure data completeness, consistency, and integrity prior to modelling or reporting.
• Collaboration: Work closely with BI developers, IT teams, actuaries, and other business stakeholders to define analytical requirements and integrate models into dashboards, reports and automate recurring analysis.
• Documentation: Maintain clear and detailed documentation of analytical processes, model logic, datasets and results for transparency and reproducibility.
• Continuous improvement: Contribute to enhancing analytical frameworks, dashboards, and methodologies under the direction of the BI manager, driving innovation and best practice in analysis.

CLIENT:

• Provide expert analytical support and advisory input to internal business stakeholders.

• Translate complex data findings into clear, actionable insights that support operational and strategic decision making.

• Collaborate with business units and BI manager to ensure analytical outputs align with business priorities and reporting needs.

• Partner with the Senior Data Analyst to ensure models, metrics and dashboards align with analytical and business reporting needs.

• Validate and cross-check data and analytical deliverables to ensure accuracy and consistency before stakeholder presentations.

• Present analytical findings through visualisation, reports, and presentations that are accessible to both technical and non-technical audiences.

• Support the BI manager during stakeholder engagements by preparing analysis, explaining methodologies and providing data driven recommendations.

• Maintain professionalism, analytical integrity, and confidentiality when handling business critical information.

PEOPLE

• Lead a team of data professionals and maintain a high impact culture.

• Act as technical mentor and subject matter expert, guiding junior analysts in statistical methods, data storytelling, and visualisation best practices.

• Share knowledge, analytical techniques, and tools that enhance team's collective analytical capability.

• Support the BI manager in cultivating a collaborative and insight driven team culture.

• Promote continuous learning and professional growth by staying current with new analytical methods, tools and technologies.

• Foster teamwork and open communication by contributing to regular knowledge sharing sessions and analytical reviews.

• Demonstrate accountability, professionalism and proactive approach to problem solving in all interactions.

• Encourage innovation, adaptability, and analytical curiosity within the analytics function.

FINANCE

• Deliver high-quality analytical outputs that supports financial planning, forecasting and performance tracking,

• Conduct data driven analyses to identify cost drivers, efficiency opportunities and revenue improvement areas.

• Validate financial and operational data used in dashboards, reports, and business reviews to ensure accuracy and reliability.

• Provide analytical insights that assist management in evaluating business performance and investment outcomes.

• Support the BI manager in preparing financial dashboards, KPI's and executive reporting packs for decision making.

• Ensure compliance with internal data standards, governance, and confidentiality requirements when handling financial information.

Competencies

• Examining Information

• Developing Strategies

• Directing People

• Interpreting Data

• Articulating Information

• Valuing Individuals

• Providing Insights

• Challenging Ideas

• Attention to detail