Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) in London

Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) in London

London Full-Time 70000 - 90000 £ / year (est.) Home office (partial)
EasyPark

At a Glance

  • Tasks: Architect data services and enhance performance for global machine learning products.
  • Company: Join EasyPark, a leader in innovative data solutions.
  • Benefits: Flexible hybrid working, competitive salary, and impactful projects.
  • Why this job: Make a difference in the world of data and machine learning.
  • Qualifications: Extensive experience in data applications, Python, Spark, and AWS.

The predicted salary is between 70000 - 90000 £ per year.

EasyPark is looking for a Staff Software Engineer to join their Data Science Products team in Greater London. You will architect data services and improve performance, ensuring reliable machine learning products for drivers worldwide.

The ideal candidate has extensive experience in data-intensive applications and excels in Python, Spark, and AWS.

Offering flexible hybrid working, this role allows engineers to have a significant cross-team impact.

Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) in London employer: EasyPark

EasyPark is an exceptional employer that fosters a collaborative and innovative work culture, allowing Staff Data Engineers to thrive in a flexible hybrid environment. With a strong focus on employee growth and development, team members are encouraged to take on significant cross-team projects that enhance their skills while contributing to impactful machine learning solutions for drivers globally. Located in Greater London, EasyPark offers a vibrant atmosphere that supports work-life balance and professional advancement.

EasyPark

Contact Details:

EasyPark Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) in London

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 EasyPark!

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 Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) at EasyPark.

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 EasyPark.

Apply Directly through Our Website

When you find a suitable opening like Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) at EasyPark, 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 Staff Data Engineer: Lead Scalable ML Pipelines (Hybrid) in London

Data Architecture
Machine Learning
Python
Spark
AWS
Data Engineering
Performance Optimisation

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 EasyPark, 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 EasyPark. 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 EasyPark

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 EasyPark!

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.