Senior Data Scientist (Operations)

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:
Ticket classification - Classifying the theme and problem area for each ticketSentiment analysis - Determine customer satisfaction at the end of each ticket, and identify areas with systematically low satisfactionAutomatic resolution - Developing custom models (using LLMs and other approaches) to automatically answer and resolve ticketsStaff Tooling - Developing models that provide our own team with timely answersThis 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 ticketsDevelop and maintain taxonomies for ticket themes and problem areasMonitor and improve classification accuracy through regular model evaluationsCollaborate with support teams to validate classification resultsDrive Customer Sentiment Analysis Initiatives Build sentiment analysis models to track customer satisfaction trendsDevelop dashboards and reporting systems for sentiment trackingIdentify systematic patterns in customer dissatisfactionCreate early warning systems for declining satisfaction metricsDevelop Automated Ticket Resolution Systems Research and implement appropriate LLM solutions for ticket automationDesign fallback mechanisms and confidence thresholds for automated responsesMonitor and optimise automation rates while maintaining qualityEnsure compliance with security and privacy requirementsCreate Advanced Support Staff Tools Build ML-powered tools to assist support staff in ticket resolutionDevelop knowledge retrieval systems for quick answer suggestionsImplement predictive models for ticket prioritisationDesign intuitive interfaces for tool adoptionLead Stakeholder Engagement and Project Management Present findings and recommendations to senior leadershipDefine success metrics and track project outcomesCoordinate with cross-functional teams including Operations and EngineeringDrive data-informed decision making across the organisationMaintain Technical Excellence and Innovation Stay current with latest developments in applied ML and LLMsEvaluate and implement new technologies and approachesDocument technical solutions and best practicesMentor team members on ML/AI implementationWhat experience might you have? Must-haves: 7+ years of experience in applied data science, including 3+ years leading ML/AI projects in production environmentsDemonstrated experience building and deploying NLP/LLM systems at scale, including prompt engineering and fine-tuningTrack record of architecting and implementing high-volume, real-time ML systemsExperience optimising business operations using AI/ML, preferably in customer service or support functionsHistory of leading cross-functional technical projects and influencing senior stakeholdersTechnical Expertise: Competent Python programming with focus on production-grade code and system architectureDeep 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 systemsStrong knowledge of real-time data processing architectures and high-volume system designExperience implementing and maintaining customer-facing AI systems with strict SLA requirementsExpertise in A/B testing and statistical analysis for model validation in production environmentsLeadership & Communication: Experience presenting to and influencing C-level stakeholdersTrack record of leading technical teams or mentoring junior data scientistsProven ability to translate complex technical concepts for non-technical audiencesHistory of successful collaboration with product, engineering, and operations teamsSystem Design & Architecture: Experience designing fault-tolerant, scalable ML systemsKnowledge of cloud architecture (AWS/GCP/Azure) for large-scale ML deploymentsUnderstanding of cost optimisation strategies for LLM-based systemsExperience with real-time monitoring and alerting systemsNice to have: Understanding of customer support operations, ticket management systems, and support workflowsExperience with support ticket classification systemsKnowledge of sentiment analysis and customer satisfaction metricsBackground in developing internal tooling for support teamsExperience 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 needs10 days Annual Leave Buy-Back scheme - for if you'd like some additional time off12 weeks paid Maternity Leave and 4 weeks paid Paternity Leave for employees with over 12 months serviceSpecial Leave for In Vitro Fertilisation (IVF) and other fertility treatmentsSalary 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 growthThe 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 PaymentsWorkplace nurseriesCycle to WorkGym membershipsMedical or Life InsuranceHealthcare cash plans, etcAt 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.


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