At a Glance
- Tasks: Lead and develop high-profile ML products while collaborating with diverse teams.
- Company: Join a forward-thinking tech company focused on innovation and impact.
- Benefits: Enjoy autonomy, competitive salary, and the chance to shape best practices.
- Other info: Be part of a dynamic team with opportunities for growth and collaboration.
- Why this job: Make a real difference in ML solutions that impact businesses and merchants.
- Qualifications: 7+ years in ML software engineering and strong leadership skills required.
The predicted salary is between 70000 - 90000 £ per year.
We are seeking an experienced and visionary ML Software Engineering Lead to serve as the technical and functional leader for the Data Science Enablement engineering function, owning the production development and ongoing operations of high-profile ML products.
This role balances strategic leadership with hands‑on technical contribution, guiding the architecture, design, and deployment of ML solutions while collaborating closely with product, data science, platform engineers, and other teams.
Responsibilities
- Strategy and
Vision: Define the technical vision and strategy for ML software engineering initiatives, align them with business goals, develop scalable real‑time decisioning engines, enable rapid experimentation with robust, scalable, and secure deployment.
- Engineering and
- Operational
Excellence: Establish engineering standards, operating practices, and technical governance; mentor engineers; promote continuous improvement and knowledge sharing; drive tooling, automation, observability, and operational process improvements; enforce reliability, observability, incident management, and support standards.
- Technical Leadership and
Contribution: Guide architecture, implementation, deployment, and operation of ML products and reusable components; ensure scalability, latency, explainability, and regulatory compliance; promote best practices; stay abreast of industry trends; contribute to QA and code as needed.
- Cross‑Functional
Collaboration: Partner with research‑focused data science teams, business stakeholders, infrastructure support, data engineering, security/compliance teams; collaborate with other leaders to establish an operating model for machine learning R&D; communicate complex technical concepts to non‑technical stakeholders.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related field (Ph D a plus).
- 7+ years of ML software engineering, ops, engineering, or research experience.
- 5+ years of experience deploying large‑scale, real‑time ML models in customer‑facing production environments.
- 2+ years of technical leadership experience on an early‑stage ML software engineering team.
- 2+ years of data science research experience.
- Proven experience developing microservices at scale (API design, monitoring, deployment strategies, containerization) in a cloud environment, preferably AWS and Databricks.
- Strong understanding of the data science/ML research process, software engineering, MLOps, and Dev Ops best practices.
- Strong Python skills, including Pandas, Num Py, scikit‑learn; proficiency in SQL and No SQL databases.
- Excellent communication, leadership, and stakeholder management skills.
- Bonus: Experience in merchant acquiring, payment service provider, or card network; tokenization, real‑time payments, authorization lifecycle; large regulated industry; agile environment.
Benefits
- Impact: Technical ownership of high‑profile ML products delivering business impact to merchants.
- Autonomy: End‑to‑end technical ownership, freedom to drive solutions and shape best practices.
- Collaboration: Cross‑functional high‑performing team where expertise is valued and contributions make a real difference.
- #J-18808-Ljbffr
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We think this is how you could land ML Software Engineering Lead in London
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We think you need these skills to ace ML Software Engineering Lead in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Worldpay (UK) Limited, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Worldpay (UK) Limited. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Worldpay (UK) Limited
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
