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- Tasks: Lead the development of innovative machine learning solutions and oversee project lifecycles.
- Company: Join Williams Lea, a global leader in tech-enabled business support services.
- Benefits: Enjoy 25 days holiday, private medical insurance, and a cycle-to-work scheme.
- Why this job: Make a real impact with cutting-edge ML technology in a fully remote role.
- Qualifications: 5+ years in machine learning, strong programming skills, and leadership experience preferred.
- Other info: Be part of a diverse team with excellent career growth opportunities.
The predicted salary is between 78000 - 117000 £ per year.
Salary: £97,500 per annum, plus company benefits
Contract: Full time, permanent
Shifts: 37.5 hours per week Mon-Fri, 8:30am-5pm with a 1-hour unpaid break
Work model: Fully remote
Williams Lea seeks a Lead Machine Learning Engineer to join our team. Williams Lea is the leading global provider of skilled, technology-enabled, business-critical support services, with long-term trusted relationships with blue-chip clients across investment banks, law firms and professional services firms.
Purpose of the Role
This role is responsible for maintaining and expanding the enterprise data model, and for developing, publishing, and maintaining business-critical reports for both internal and external stakeholders. You will collaborate closely with the Data & Analytics team, business stakeholders, and subject matter experts to solve organizational challenges through reporting, analysis, and data visualization.
Key Responsibilities
- Provide enterprise-wide expertise in data modelling, data quality management, report design, environment management, and automated data ingestion/refresh
- Act as a creative problem-solver, contributing to the full product lifecycle and maintaining an organized, scalable reporting environment
- Produce reports that inform high-level decision-making and drive revenue growth
About You
The ideal candidate is a self-starter and individual contributor who thrives in a global, fast-paced environment. You will be part of a team delivering market-changing online services, contributing your technical expertise and strong work ethic.
Working Environment
- Operate within an Agile/Scrum framework to meet the needs of a dynamic customer service and operations environment
- Be a hands-on technologist, driving best practices and helping shape the strategic direction of the IT function
- Lead a small, distributed team of engineers across the US, UK, and India, ensuring alignment with business goals and service delivery expectations
- Manage cloud-based platforms, leveraging tools like auto-scaling, Infrastructure as Code, and continuous delivery methodologies to optimize performance and accelerate delivery
Key Responsibilities:
- Machine Learning Solution Development: Design, develop and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors
- Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as-code tools like AWS CloudFormation and Terraform to automate model deployment and system setup
- Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of ML projects
- Client & Stakeholder Collaboration: Collaborate with project managers and stakeholders across a range of sectors to gather requirements and translate business needs into technical solutions. Present findings and ML model results to non-technical audiences in a clear manner, and refine solutions based on their feedback
- Quality, Security & Compliance: Ensure that ML solutions meet quality and performance standards. Implement monitoring and logging for models in production, and proactively improve model accuracy and efficiency. Given the sensitive nature of our data, enforce data security best practices and compliance with relevant regulations (e.g. data privacy and confidentiality) in all ML workflows
Required Qualifications & Experience:
- Education: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field. Strong foundation in statistics and algorithms is expected
- Experience: 5+ years of hands-on experience in machine learning or data science roles, with a track record of building and deploying ML models into production. Prior experience leading projects or teams is a plus for a lead role
- Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous
- Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena/Redshift, etc.) is expected. Strong knowledge of DevOps/MLOps practices – candidates should have built or worked with CI/CD pipelines for ML, using tools like Docker and Jenkins, and infrastructure-as-code tools like CloudFormation or Terraform to automate deployments
- Hybrid Work Skills: Ability to thrive in a hybrid work environment – should be self-motivated and communicative when working remotely, and effective at collaboration
- Soft Skills: Excellent problem-solving and analytical thinking. Strong communication skills to explain complex ML concepts to clients or management. Ability to work under tight deadlines and multitask across projects for different clients. A client-focused mindset is essential, as the role involves understanding and addressing the needs of large clients who come to us because they trust us
Preferred Experience:
- Domain Knowledge: Familiarity with use-cases like document classification, contract analytics, fraud/risk modelling, or NLP on legal texts will help the engineer design better domain-tailored solutions
- Certifications: Relevant certifications such as AWS Certified Machine Learning – Specialty or AWS Solutions Architect, and any Machine Learning/Deep Learning specialisations, will be a plus (demonstrating validated expertise)
- Tools & Frameworks: Experience with collaborative software development tools and practices (Git version control, code review), and with project management tools (JIRA or similar) in an agile environment. Familiarity with other ML Ops tools (Kubeflow, MLflow, etc.) or big data processing frameworks (Spark) can be an added advantage
Rewards and Benefits
We believe in supporting our employees in both their professional and personal lives. As part of our commitment to your well-being, we offer a comprehensive benefits package, including but not limited to:
- 25 days holiday, plus bank holidays (pro-rata for part time roles)
- Salary sacrifice schemes, retail vouchers – including our TechScheme which can be used on a range of gadgets such as Smart TVs, laptops and computers or household appliances.
- Life Assurance
- Private Medical Insurance
- Dental Insurance
- Health Assessments
- Cycle-to-work scheme
- Discounted gym memberships
- Referral Scheme
You will also have the opportunity to work for a global employer who is dedicated to offering each and every employee an enjoyable, challenging and rewarding career with future career development prospects.
Equality and Diversity
The Company values the differences that a diverse workforce brings to the organisation and will not discriminate because of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race (which includes colour, nationality and ethnic or national origins), religion or belief, sex or sexual orientation (each of these being a "protected characteristic" in discrimination law). It will not discriminate because of any other irrelevant factor and will build a culture that values openness, fairness and transparency.
If you have a disability and would prefer to apply in a different format or would like to make a reasonable adjustment to enable you to make an interview please contact us at (we do not accept applications to this email address).
View our Privacy Notice. Please note: We can only consider candidates who are currently based in and have the legal right to work in the UK.
Lead Machine Learning Engineer employer: Williams Lea
Contact Detail:
Williams Lea Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend virtual meetups, and engage with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those that highlight your experience with AWS and MLOps. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and be ready to discuss your past projects in detail. Practice explaining complex ideas in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Williams Lea.
We think you need these skills to ace Lead Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Lead Machine Learning Engineer role. Highlight your experience with ML models, AWS, and any relevant projects you've led. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Be sure to mention any specific experiences that relate to the job description.
Showcase Your Technical Skills: Don’t forget to highlight your programming skills, especially in Python and any ML libraries you’ve used. We love seeing examples of your work, so if you have a portfolio or GitHub, include that too!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Williams Lea
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially those relevant to the role. Be ready to discuss your experience with ML models, algorithms, and tools like AWS SageMaker. They’ll want to see that you can not only talk the talk but also walk the walk!
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex business challenges using data. Think about specific projects where you led the ML lifecycle or improved model accuracy. This will demonstrate your ability to think critically and creatively, which is key for this role.
✨Communicate Clearly
Since you'll be presenting findings to non-technical audiences, practice explaining your work in simple terms. Use analogies or visuals if it helps. Being able to convey complex ideas clearly will set you apart from other candidates.
✨Familiarise Yourself with Agile Practices
As the role operates within an Agile/Scrum framework, understanding these methodologies is crucial. Be prepared to discuss how you've worked in Agile teams before and how you can contribute to a dynamic environment. Highlight any experience leading teams or mentoring others as well!
