At a Glance
- Tasks: Engineer cutting-edge machine learning solutions and lead innovative projects.
- Company: Join Datatonic, a premier Google Cloud partner in AI transformation.
- Benefits: Private health insurance, gym discounts, WFH allowance, and learning platforms.
- Other info: Dynamic team culture focused on innovation and career growth.
- Why this job: Make a real impact in AI and data while working with industry experts.
- Qualifications: 3+ years in machine learning, strong Python skills, and cloud platform familiarity.
The predicted salary is between 63000 - 77000 £ per year.
At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world‑class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud. By partnering with us, clients future‑proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world.
Your Mission
As a Senior Machine Learning Engineer, you’ll know how to engineer beautiful code in Python and take pride in what you produce. You’ll advocate high‑quality engineering and best practice in production software as well as rapid prototypes. While the position is a hands‑on technical role, we’d be particularly interested to find candidates with a desire to lead projects and take an active role in leading client discussions. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements. To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You’ll be comfortable with model optimisation and deployment tools and practices. Furthermore, you’ll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud.
What You’ll Do
- Translating Requirements: Interpret vague requirements and develop models to solve real‑world problems.
- Data Science: Conduct ML experiments using programming languages with machine learning libraries.
- Optimisation: Optimise machine learning solutions for performance and scalability.
- Custom Code: Implement tailored machine learning code to meet specific needs.
- Data Engineering: Ensure efficient data flow between databases and backend systems.
- MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
- ML Architecture Design: Create machine learning architectures using Google Cloud tools and services.
- Engineering Software for Production: Build and deploy production‑grade software for machine learning and data‑driven solutions.
What You’ll Bring
- Experience: 3+ years as a Machine Learning Engineer, preferably with a consulting background.
- Programming Skills: Proficiency in Python as a backend language, capable of delivering production‑ready code in well‑tested CI/CD pipelines.
- Cloud Expertise: Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.
- Software Engineering: Hands‑on experience with foundational software engineering practices.
- Database Proficiency: Strong knowledge of SQL for querying and managing data.
- Scalability: Experience scaling computations using GPUs or distributed computing systems.
- ML Integration: Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python).
- Soft Skills: Strong communication and presentation skills to effectively convey technical concepts.
Bonus Points If You Have
Bonus points if you have consulting experience, advanced cloud architecture skills, or a background in large‑scale data pipelines.
What’s in It for You?
- Health Perks: Private health insurance (Vitality Health) and Smart Health Services.
- Fitness & Wellbeing: 50% gym membership discounts (Nuffield Health, Virgin Active, Pure Gym).
- Hybrid Model: A WFH allowance to keep you comfortable.
- Learning & Growth: Access to platforms like Udemy to fuel your curiosity.
- Pension: Auto‑enrolment after probation period. 3% employer contributions raising 1% per year of service to a max of 10%.
- Income Protection: Up to 75% of base salary, up to 2 years.
- Cycle to Work Scheme.
Why Datatonic?
Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you!
Apply
Are you ready to make an impact? Apply now and take your career to the next level.
Senior Machine Learning Engineer employer: Datatonic
Datatonic is an exceptional employer that fosters a vibrant and innovative work culture, perfect for those passionate about cloud technologies and DevOps practices. With a strong emphasis on employee growth, you will have ample opportunities to enhance your skills while working alongside talented teams on cutting-edge projects for prestigious clients. Located in a dynamic scale-up environment, Datatonic offers a unique chance to make a significant impact in the tech landscape while enjoying a collaborative and supportive atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land Senior 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 Datatonic!
✨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 Senior Machine Learning Engineer at Datatonic.
✨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 Datatonic.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Machine Learning Engineer at Datatonic, 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 Senior 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 Datatonic, 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 Datatonic. 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 Datatonic
✨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 Datatonic!
✨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.
