At a Glance
- Tasks: Lead customer analytics projects, turning complex data into clear insights for decision-making.
- Company: Join a diverse and inclusive financial services firm committed to innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous improvement and best practices.
- Why this job: Make a real impact by driving analytics that enhance customer trust and compliance.
- Qualifications: 5+ years in analytics or risk/compliance roles; strong communication and mentoring skills.
The predicted salary is between 60000 - 80000 £ per year.
The role is responsible for leading the delivery of end-to-end customer analytics engagements, translating and presenting complex data in clear, actionable insights that support decision-making and build customer trust. It designs and executes advanced analytics workflows, including automated AI/ML models and visualisations, while embedding risk and control principles into analysis and reporting to support compliance and risk awareness. The role drives continuous improvement through innovation, automation, and best practices, manages multiple projects with consistency and quality, and communicates insights effectively to internal and external stakeholders. In addition, the role provides mentoring and guidance to junior analysts, promoting high standards, collaboration, and capability development across the team.
Key Responsibilities
- Manage and deliver end-to-end customer programmes, ensuring analytics outputs are accurate, insightful, and aligned to customer objectives.
- Design, build, and maintain advanced analytics workflows, including automated models, dashboards, and visualisations.
- Translate analytical findings into actionable insights and recommendations for both technical and non-technical stakeholders.
- Integrate risk and control considerations into analytics and reporting, identifying key risk indicators and supporting compliance alignment.
- Lead structured problem-solving initiatives to improve analytics quality, efficiency, and scalability through automation and standardisation.
- Manage multiple analytics projects concurrently, ensuring consistent delivery, quality standards, and timely execution.
- Present insights clearly and confidently to internal teams and external customers, tailoring messaging to business context.
- Promote and embed best practices in analytics delivery, documentation, and data governance.
- Coach and mentor junior analysts, providing guidance, feedback, and support to build capability and confidence.
- Foster collaboration within the team and across stakeholders to deliver high-quality, integrated outcomes.
Skills & Competencies
- Customer Engagement & Delivery: Manages end-to-end customer deliverables, translating analytics into actionable insights. Builds customer trust through reliability and quality of insight and delivery.
- Intelligence, Data Analytics & AI: Designs and executes advanced analytics workflows. Builds automated models and visualisations to enhance insight depth and speed. Aids development of new AI tools and customer use cases. Utilises LLMs and data analytics tools such as Python and PowerBI to build and deliver analytics.
- Risk & Control Expertise: Integrates risk and control principles into analytics and reporting. Identifies risk indicators and supports compliance alignment.
- Problem-Solving, Innovation & Best Practices: Implements measurable process and automation improvements. Promotes consistent best practices within analytics delivery.
- Communication, Collaboration & Stakeholder Management: Presents insights effectively to internal and external stakeholders, ensuring clarity and business relevance.
- Strategic & Operational Execution: Manages multiple projects efficiently, ensuring consistency and quality. Improves processes and delivery speed.
- Mentoring & Leadership: Coaches junior analysts, promoting collaboration and quality standards within the team.
Required Experience & Qualifications
- 5+ years’ experience in a relevant financial services risk/compliance or analytics role.
CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Customer Analytics Lead employer: Cube Asia
As a leading employer in the financial services sector, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to excel. Our Customer Analytics Lead role offers not only competitive benefits and opportunities for professional growth but also a chance to make a meaningful impact through advanced analytics and customer engagement. Located in a vibrant area, we support a diverse workforce and are committed to inclusivity, ensuring that every team member can thrive and contribute to our shared success.
StudySmarter Expert Advice🤫
We think this is how you could land Customer Analytics Lead
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Cube Asia!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Cube Asia.
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We think you need these skills to ace Customer Analytics Lead
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Cube Asia, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Cube Asia. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Cube Asia
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Cube Asia!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
