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- Tasks: Lead the development of innovative ML systems and optimise pricing processes.
- Company: Join a leading fin-tech company with a focus on growth and innovation.
- Benefits: Competitive salary, bonus, shares, and hybrid working model.
- Why this job: Be a key player in shaping the future of ML in a thriving business.
- Qualifications: Degree in relevant field and proven ML lifecycle management experience.
- Other info: Exciting opportunity for career growth in a dynamic environment.
The predicted salary is between 95000 - 130000 £ per year.
Are you an innovative, decisive Machine Learning Engineer looking for your next challenge? This is your chance to join a marquee name within the fin-tech space looking to add their first Machine Learning Engineer to the business. This will require you to be a key individual contributor with the ability to make decisions yourself.
Within the role, you will drive innovation by optimising and automating pricing processes to enable faster, more accurate decision-making. Your work will focus on developing and maintaining tooling and frameworks that enhance the efficiency of our predictive models, reducing deployment times, increasing scalability, and improving model performance through regular updates and monitoring. You will work closely with the Data Scientists, Actuaries, and Product team to deliver scalable, production-grade ML systems. This is a super exciting time to join the business who, after a number of years of great success, have hit profitability and now want to grow through strategic hiring.
Key Responsibilities:- Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models).
- Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries.
- Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work.
- Collaborate with the technical pricing, street pricing and product teams to gather requirements and feedback on tooling and to build impactful technology.
- Communicate complex concepts to technical and non-technical stakeholders through clear storytelling.
- Education: Bachelor's or Master's degree in Statistics, Data Science, Computer Science or related field.
- Experience: Proven experience in ML model lifecycle management.
- Core Competencies: Model lifecycle: You've got hands-on experience with managing the ML model lifecycle, including both online and batch processes.
- Statistical Methodology: You have worked with GLMs and other machine learning algorithms and have in-depth knowledge of how they work.
- Python: You have built and deployed production-grade Python applications and you are familiar with data science libraries such as pandas and scikit-learn.
Lead Machine Learning Engineer employer: Burns Sheehan
Contact Detail:
Burns Sheehan 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 fin-tech space and let them know you're on the lookout for opportunities. A personal recommendation can go a long way in landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to model lifecycle management. 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 your storytelling skills. Be ready to explain complex ML concepts 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! We’re always looking for innovative minds like yours, and applying directly can help you stand out from the crowd. Plus, it shows you're genuinely interested in joining us!
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 model lifecycle management and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you're the perfect fit for this role. Share specific examples of how you've driven innovation in previous positions, especially in optimising processes or collaborating with teams.
Showcase Your Technical Skills: Don’t forget to showcase your technical skills in Python and any experience with data science libraries like pandas and scikit-learn. We love seeing practical examples of how you've built and deployed production-grade applications!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Burns Sheehan
✨Know Your ML Lifecycle
Make sure you can confidently discuss your experience with managing the ML model lifecycle. Be prepared to share specific examples of how you've handled both online and batch processes, as this will show your hands-on expertise.
✨Brush Up on Statistical Methodologies
Familiarise yourself with Generalised Linear Models (GLMs) and other machine learning algorithms. You might be asked to explain how these methodologies work, so having a solid understanding will help you impress the interviewers.
✨Showcase Your Python Skills
Be ready to talk about your experience building and deploying production-grade Python applications. Highlight any projects where you've used data science libraries like pandas and scikit-learn, as this is crucial for the role.
✨Communicate Clearly
Practice explaining complex concepts in simple terms. You'll need to communicate effectively with both technical and non-technical stakeholders, so being able to tell a clear story about your work will set you apart.
