Wagestream is on a mission to bring better financial wellbeing to frontline workers.

We partner with some of the world’s most famous employers, like Bupa, Burger King, Greene King and the mighty NHS to give their teams access to fairer financial services - all built around flexible pay. Over three million people can now choose how often they’re paid, track their shifts and earnings, start saving, use budgeting tools, get free financial coaching, and access fairer financial products. All in one financial wellbeing super-app.

Wagestream is unique: VC-backed and growing at scale-up pace, but with a social conscience. Some of the world's leading financial charities and impact funds were our founding investors, and we operate on a social charter - which means every product we build has to improve financial health and reduce the $5.6bn ‘premium’ lower-income earners pay for financial services each year.

You’d be joining a team of over 200 passionate, ambitious people, across Europe and the USA, building a category-leading fintech product and all united by that same mission.

The Opportunity:

At Wagestream, we’re revolutionising financial wellbeing by enabling access to flexible pay solutions that empower individuals to take control of their finances. In the process of delivering our products to over a million customers, we receive approximately 20,000 support tickets per month.

We are now looking for a Senior Data Scientist to embed into our Operations team and lead on challenges such as:

1. Ticket classification - Classifying the theme and problem area for each ticket
2. Sentiment analysis - Determine customer satisfaction at the end of each ticket, and identify areas with systematically low satisfaction
3. Automatic resolution - Developing custom models (using LLMs and other approaches) to automatically answer and resolve tickets
4. Staff Tooling - Developing models that provide our own team with timely answers

This role will have significant independence and autonomy, and will involve using tooling, methods and products at the cutting edge of applied machine learning. This role will involve exposure to several senior stakeholders, and is a great opportunity to accelerate your career.

The Team:

Sitting in the operations function you will report directly to the VP of Operations while collaborating heavily with Wagestream’s Engineering, TechOps and Support teams to realise impactful solutions.

What will you be doing?

Lead End-to-End Machine Learning Projects for Ticket Classification

• Design and implement ML pipelines to accurately categorise 20,000 monthly support tickets
• Develop and maintain taxonomies for ticket themes and problem areas
• Monitor and improve classification accuracy through regular model evaluations
• Collaborate with support teams to validate classification results

Drive Customer Sentiment Analysis Initiatives

• Build sentiment analysis models to track customer satisfaction trends
• Develop dashboards and reporting systems for sentiment tracking
• Identify systematic patterns in customer dissatisfaction
• Create early warning systems for declining satisfaction metrics

Develop Automated Ticket Resolution Systems

• Research and implement appropriate LLM solutions for ticket automation
• Design fallback mechanisms and confidence thresholds for automated responses
• Monitor and optimise automation rates while maintaining quality
• Ensure compliance with security and privacy requirements

Create Advanced Support Staff Tools

• Build ML-powered tools to assist support staff in ticket resolution
• Develop knowledge retrieval systems for quick answer suggestions
• Implement predictive models for ticket prioritisation
• Design intuitive interfaces for tool adoption

Lead Stakeholder Engagement and Project Management

• Present findings and recommendations to senior leadership
• Define success metrics and track project outcomes
• Coordinate with cross-functional teams including Operations and Engineering
• Drive data-informed decision making across the organisation

Maintain Technical Excellence and Innovation

• Stay current with latest developments in applied ML and LLMs
• Evaluate and implement new technologies and approaches
• Document technical solutions and best practices
• Mentor team members on ML/AI implementation

What experience might you have?

Must-haves:

• 7+ years of experience in applied data science, including 3+ years leading ML/AI projects in production environments
• Demonstrated experience building and deploying NLP/LLM systems at scale, including prompt engineering and fine-tuning
• Track record of architecting and implementing high-volume, real-time ML systems
• Experience optimising business operations using AI/ML, preferably in customer service or support functions
• History of leading cross-functional technical projects and influencing senior stakeholders

Technical Expertise:

• Competent Python programming with focus on production-grade code and system architecture
• Deep experience with modern LLM frameworks (e.g., LangChain, OpenAI API) and ML frameworks (PyTorch, TensorFlow)
• Proven experience with MLOps, including monitoring, maintenance, and cost optimisation of production ML systems
• Strong knowledge of real-time data processing architectures and high-volume system design
• Experience implementing and maintaining customer-facing AI systems with strict SLA requirements
• Expertise in A/B testing and statistical analysis for model validation in production environments

Leadership & Communication:

• Experience presenting to and influencing C-level stakeholders
• Track record of leading technical teams or mentoring junior data scientists
• Proven ability to translate complex technical concepts for non-technical audiences
• History of successful collaboration with product, engineering, and operations teams

System Design & Architecture:

• Experience designing fault-tolerant, scalable ML systems
• Knowledge of cloud architecture (AWS/GCP/Azure) for large-scale ML deployments
• Understanding of cost optimisation strategies for LLM-based systems
• Experience with real-time monitoring and alerting systems

Nice to have:

• Understanding of customer support operations, ticket management systems, and support workflows
• Experience with support ticket classification systems
• Knowledge of sentiment analysis and customer satisfaction metrics
• Background in developing internal tooling for support teams
• Experience with specific support platforms (Zendesk, Intercom, etc.)

Salary: Dependant on experience, from £80,000 + bonus & equity

Working Policy: Hybrid, with three office days per week

Benefits:

• 25 Days Annual Leave in addition to public holidays (up to 5 day rollover), as well as flexible time off allowances for any ad-hoc childcare/family/caring needs
• 10 days Annual Leave Buy-Back scheme - for if you’d like some additional time off
• 12 weeks paid Maternity Leave and 4 weeks paid Paternity Leave for employees with over 12 months service
• Special Leave for In Vitro Fertilisation (IVF) and other fertility treatments
• Salary sacrifice to pension, as well as bonus exchange to Pension: reap even more rewards of any bonus by paying into your pension & save on Tax and NI + added compound growth
• The best benefit of all, access to Wagestream!
• Access to Salary Sacrifice Scheme - Ben -THE Benefits marketplace.Choose the benefits you want, when you want. Pay less tax, receive more value 🎉

Additional:

• Additional Pension Payments
• Workplace nurseries
• Cycle to Work
• Gym memberships
• Medical or Life Insurance
• Healthcare cash plans, etc

At Wagestream we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Wagestream is an equal opportunity workplace. We are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, or veteran status.

Department

Data

Locations

London Office

Remote status

Hybrid Remote

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