Lead Machine Learning Scientist, Customer Operations

About the role

This role advances customer operations by applying machine learning to reduce effort and improve issue resolution. It leads human-in-the-loop systems that blend automation with support workforce capabilities to deliver measurable impact at scale.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Machine learning uncovers impactful opportunities across Customer Operations, defining scope and accelerating prediction and routing of customer issues. Technical leadership elevates Machine Learning expertise across the discipline, guiding colleagues toward best practices in model development through mentoring.

Requirements

The posting states a bachelor's degree requirement. The posting states a minimum of 17 years of experience.

A multiple year track record of excellence in leading development and deployment of advanced Machine Learning models to solve real business problems in a fast-moving tech company is required. Experience developing and shipping deep learning, graph-based, and sequence-based ML architectures to production and delivering business impact is necessary. An impact driven mindset owns the end-to-end journey from business problem to measurable production outcomes without needing direction. A self-starter mindset proactively identifies issues and opportunities and tackles them independently rather than waiting for instructions. Daily use of production Python and SQL is required, with comfort in learning Go lang for backend microservices in the company. Comfort working within ambiguous team environments is needed to resolve uncertainty with peers and stakeholders during model development. A product mindset focuses on customer outcomes and data-informed decisions to ensure solutions align with user needs. Eagerness to communicate fast-moving Machine Learning advances to colleagues without specialized domain knowledge is essential for cross-functional collaboration. Adaptability, curiosity, and enjoyment of learning new technologies and ideas support continuous improvement in production systems.

Nice to have

Experience in customer operations, capacity planning, forecasting, or regulated institutions helps prioritize operational constraints. Commercial experience writing critical production code and working with microservices improves reliability and deployment practices.

Practical notes

This role requires sponsorship for UK work eligibility and may involve ad hoc travel. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning operations combine data science, software engineering, and business workflows to deploy models that affect customer outcomes. Large language models and retrieval augmented generation are used to enhance automated decision making in customer service. Cross-functional product squads integrate data scientists, engineers, and operations staff to iterate quickly. Production systems demand rigorous monitoring, versioning, and testing of models in live environments. Clear communication of technical concepts to non-technical stakeholders is a core part of the role.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.