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Lead Data Scientist - Contact Automation
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Lead Data Scientist - Contact Automation
Job Description: Data Science Lead (Contact Automation) — Wise London
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About the Company
Wise is a global technology company, building the best way to move and manage the world’s money. With a mission of "Min fees. Max ease. Full speed.", Wise simplifies cross-border payments, enabling billions of users and businesses to send, receive, and spend money internationally effortlessly.
As a core contributor to their team, you’ll play a critical role in creating an entirely new system to revolutionise how the world’s money is managed—for everyone, everywhere.
Job Description
We are seeking a Data Science Lead to join our Contact Automation team in London.
This role offers a unique opportunity to work at the heart of an intelligence system for Wise’s contact automation platform. Your work will identify customer support challenges, develop solutions that help customers self-resolve issues, and enhance the overall agent experience. Directly impacting millions of Wise’s customers, your innovations will drive the company’s mission to make global financial transactions effortless.
About the Role
The goal is to build an automated Wisdom Assistant, which can handle a majority of customer queries natively within the chat interface—ranging from simplicity to the most complex interactions. This reduction in friction aligns with Wise’s core philosophy: “Moving money should not be difficult.”.
As Data Science Lead, you will:
How You’ll Contribute
Evaluation & Experimentation (Core)
- Own the design of online and offline evaluation frameworks for LLM-based systems, ensuring they remain scalable and actionable.
- Build and scale A/B testing infrastructure to measure real-world customer impact, guiding data-informed product decisions.
- Define meaningful outcome-oriented metrics that connect system performance to tangible customer and business benefits.
System Performance & Reliability
- Identify key failure modes (reasoning, retrieval, tool use, tone, etc.) across the assistant’s workflow, and drive continuous improvements.
- Collaborate closely with software engineers to iterate on efficient and scalable agentic workflows.
- Optimise system behaviour through data-driven prompt engineering, evaluation insights, and iterative refinement.
- Ensure a high degree of reliability across multiple languages and geographies.
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Opportunity Identification
- Analyse large-volume conversation logs to surface critical gaps in the customer experience, spotting untapped automation opportunities.
- Shape the strategic roadmap by translating ambiguous problems into tangible data-driven targeted improvements.
- Bridge technical and cross-functional insights to secure actionable prioritisation.
Cross-Functional Collaboration
- Partner daily with Product, Engineering, and Design teams to shape the assistant’s tone, accuracy, and usability.
- Influence product direction through activable data narratives—intelligently connecting findings to realistic solutions.
- Garner trust by ensuring the system upholds high standards of customer insight, clarity, and trustworthiness.
How You’ll Apply This
- Operate autonomously in a high-velocity environment, typically within 1–2 week cycles, while balancing ownership of immediate priorities with ambitious long-term goals.
- Leverage feedback loops (both human-related features and machine-generated) to refine the assistant continuously.
- Expand beyond your defined scope flexibly when needed to propel the overall initiative forward.
Requirements
We’re seeking a highly self-motivated, technically resourceful individual with a proven record of delivering real-world value. Key capabilities include:
- Data ownership mindset – You build and drive ideas from experimentation to production, with a clear focus on customer-facing impact.
- Technical clarity – Capable of translating complex data insights to both technical and non-technical stakeholders, leading to actionable next steps.
- Deep applied statistics foundation – Mastery of A/B testing, experimentation, and causal inference, particularly at scale.
- Primer in model evaluation – Strong understanding of measuring classification, regression, or LLM-based systems and connecting their performance to business impact.
- Product sensibility – Profound ability to work collaboratively in a cross-disciplinary environment to improve both the quality and user experience of automation solutions.
- Production coding experience – Proficiency in writing code for Python, TypeScript, or Java environments.
- LLM & Agentic Architecture Exposure – Familiarity with modern frameworks (e.g., Mistral, LangGraph, operating multi-agent setups) considered an advantage.
- Data proficiency – Hands-on experience with SQL, Snowflake, and data visualisation tools.
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Nice-to-Haves
It will be particularly valuable to have:
- Experience working on customer-facing agentic systems with direct UI integrations.
- Exceptional Bayesian analysis & A/B testing skills.
- Background in operationalising recommendation or customer experience systems with real-world outcomes.
Company Culture & Openness
Beyond Technical Expertise, We Value:
₋ No borders – Diversity of background, perspective, and identity creates better products. ₋ Openness – Wise is committed to fostering a culture where everyone belongs, feels judged nor prejudiced. ₋ Mission-driven passions – If you care about improving financial access globally and enjoy refining game-changing systems, this could be a perfect fit for you.
We encourage underrepresented voices, especially in non-traditional academic backgrounds, to apply: Your experience will be more valuable than any degree.
If you’d like to learn more about life at Wise, visit Wise.Jobs and explore opportunities by following:
@η-wise-₍based-in@ Instagram | @wise | LinkedIn
At the heart of our team are happier users, smarter systems, and an ever-improving digital infrastructure. Let’s make money move for the whole world.
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