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For Companies At a Glance
- Tasks: Drive innovative data solutions and develop predictive models for corporate risk and broking.
- Company: Join a leading firm focused on advanced analytics and AI in the financial sector.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact by transforming data into actionable insights and solutions.
- Qualifications: Experience in data analytics, model development, and strong stakeholder management skills.
- Other info: Dynamic role with a focus on collaboration and continuous improvement.
The predicted salary is between 36000 - 60000 £ per year.
Reporting into Tech Data Leadership, this role is pivotal in driving solutioning around Corporate Risk and Broking (CRB) data challenges and developing insights through advanced analytics and AI, developing predictive models and client applications to support risk and broking. The Data Analyst / Solution Architect actively manages stakeholder relationships, ensuring that business needs are translated into actionable data solutions. A key focus is on tracing and documenting data lineage across systems, guaranteeing transparency, compliance, and trust in all data-driven initiatives. The role balances analytics and model development with client application integration, operating as a bridge between technical teams and business stakeholders to deliver impactful, high-quality solutions.
Responsibilities:
- Analytics & Model Development – Creating Insights, Ensuring Data Lineage
- Collaborate closely with product owners, engineers, and business stakeholders to develop solutions to data related challenges and ensure solutions align with business needs and priorities.
- Develop predictive models for broking and revenue forecasting using corporate platforms (e.g., Python, scikit-learn, TensorFlow).
- Analyze CRB data (e.g., client portfolios, market feeds) to generate actionable insights for business stakeholders.
- Build revenue models to optimize R&B financial outcomes.
- Trace and document data lineage across all relevant systems, ensuring data sources, transformations, and usage are transparent and auditable.
- Ensure model accuracy, robustness, and compliance through rigorous testing and validation.
- Develop AI-integrated client applications (e.g., automated broking tools) for R&B workflows.
- Work with engineers to integrate models into data products and dashboards.
- Support data presentation efforts, ensuring insights are actionable for brokers/advisors.
- Monitor application performance, incorporating feedback for improvements.
- Foster strong stakeholder relationships by gathering feedback, incorporating user needs, and driving continuous improvement.
- Monitor application performance and data flows, proactively addressing issues related to data quality, lineage, and user experience.
Data Solution Architect in London employer: WTW
Contact Detail:
WTW Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Solution Architect in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and analytics projects. This gives you a chance to demonstrate your expertise and makes you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by understanding the company’s data challenges. Research their current projects and think about how your skills can help solve their problems. Tailoring your approach shows you're genuinely interested.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Data Solution Architect in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Solution Architect. Highlight your experience with predictive models, data lineage, and stakeholder management. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data solutions and how you can bridge the gap between technical teams and business stakeholders. Let us know what excites you about this role!
Showcase Relevant Projects: If you've worked on projects involving AI, analytics, or client applications, make sure to include them in your application. We love seeing real-world examples of how you've tackled data challenges and delivered impactful solutions.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at WTW
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of CRB data and how it impacts business decisions. Brush up on your knowledge of predictive modelling techniques and tools like Python, scikit-learn, and TensorFlow. Being able to discuss these confidently will show that you’re ready to tackle the challenges head-on.
✨Showcase Your Stakeholder Management Skills
Prepare examples of how you've successfully managed stakeholder relationships in the past. Think about times when you translated complex data needs into actionable solutions. This role is all about bridging the gap between technical teams and business stakeholders, so demonstrating your communication skills is key.
✨Demonstrate Your Analytical Mindset
Be ready to discuss how you approach analytics and model development. Share specific instances where your insights led to impactful business outcomes. Highlight your experience with tracing and documenting data lineage, as this is crucial for ensuring transparency and compliance in data-driven initiatives.
✨Prepare for Technical Questions
Expect to face some technical questions related to data integration and AI-driven solutions. Brush up on your understanding of client application integration and be prepared to discuss how you would monitor application performance and address data quality issues. Showing your technical prowess will set you apart from other candidates.
