At a Glance
- Tasks: Build and run impactful ML systems, collaborating with Data Scientists and engineering teams.
- Company: Innovative company committed to diversity and inclusion.
- Benefits: Hybrid working, flexible hours, 25 days leave, and career development opportunities.
- Other info: Inclusive culture with a focus on work-life balance and personal growth.
- Why this job: Make a real impact in tech while growing your skills in a supportive environment.
- Qualifications: Experience with ML platforms, Python, and cloud environments; strong collaboration skills.
The predicted salary is between 60000 - 80000 £ per year.
We’re looking for a Machine Learning Engineer to help build and run production‑ready ML systems that make a real impact across the business.
You’ll work closely with Data Scientists and engineering teams, shaping the ML roadmap, developing scalable solutions, and driving innovation while growing your career.
You’ll make an impact by
- Build and automate ML pipelines for feature engineering, model training, and model scoring using Python, Py Spark, Databricks, and MLflow.
- Productionise Data Science models, converting notebooks into modular, tested, production‑ready code.
- Deploy models into batch and real‑time environments, managing versioning, promotion, rollback, and scheduled workflows via MLflow and APIs.
- Implement monitoring and observability, including data and model drift detection, performance alerts, logging, and automated retraining.
- Collaborate with Data Engineering and Platform teams on CI/CD integration, pipeline performance, compute optimisation, and secure deployment patterns.
- Maintain engineering standards, ensuring high‑quality testing, documentation, code quality, reproducibility, and operational reliability.
Your skills and experience
- Experience with ML platforms including Databricks, MLflow, Delta Lake, and cloud environments.
- Proficient in Python, Py Spark, and SQL, following production coding best practices.
- Understanding of data, distributed ML pipelines, and model deployment patterns, including monitoring, drift detection, and lifecycle operations.
- Exposure to CI/CD, containerisation, and API integration, with the ability to build scalable, production‑ready ML systems.
- Comfortable working technically while communicating effectively with Data Scientists, stakeholders, and cross‑functional teams.
Why You’ll Love It Here
- Annual discretionary bonus
- Up to 11% pension contributions
- Hybrid working + flexible hours
- 25 days annual leave + bank holidays + buy/sell options
- Career development and mentoring
- Inclusive culture + employee networks
- Share investment options
Our DEI Commitment
We celebrate individuality and believe our differences make us stronger.
We’re proud to foster a culture where everyone feels respected, valued, and empowered to thrive.
As an Equal Opportunity and Disability Confident Employer, we ensure fair consideration for all applicants and offer interviews to all disabled candidates who meet the essential criteria.
We understand that everyone’s circumstances are different and are happy to explore flexible working options such as reduced hours or job shares to support work–life balance.
If you meet the core criteria but not every requirement, we’d still love to hear from you.
Let’s explore how this role could support your next career step.
If you need adjustments during the recruitment process, just let us know we’re here to support you.
#J-18808-Ljbffr
Machine Learning Engineer employer: Intact Insurance
At Intact Insurance, we pride ourselves on being an exceptional employer, offering a dynamic work culture that champions growth and innovation. As a DevOps & Automation Platform Services Manager in London, you'll benefit from a supportive environment with flexible working options, comprehensive career development opportunities, and a commitment to diversity and inclusion. Join us to make a meaningful impact in the insurance industry while enjoying competitive benefits like an annual bonus, generous pension contributions, and a strong focus on work-life balance.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Intact Insurance!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Machine Learning Engineer at Intact Insurance.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Intact Insurance.
✨Apply Directly through Our Website
When you find a suitable opening like Machine Learning Engineer at Intact Insurance, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Machine Learning Engineer
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Intact Insurance, 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 Intact Insurance. 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 Intact Insurance
✨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!
✨Showcase Your Projects
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
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Intact Insurance!
✨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.
