Lead Machine Learning Engineer

Lead Machine Learning Engineer

Full-Time 63000 - 77000 £ / year (est.) Home office (partial)
Stryker Corporation

At a Glance

  • Tasks: Lead the development of innovative ML/AI services and nurture a high-performing data science team.
  • Company: Join Kingfisher, a diverse team of over 74,000 passionate individuals.
  • Benefits: Enjoy flexible working hours, competitive salary, and a supportive environment for growth.
  • Other info: Embrace a culture of inclusivity, curiosity, and accountability while driving innovation.
  • Why this job: Make a real impact in the home improvement industry with cutting-edge technology.
  • Qualifications: Proven experience in AI products, cloud-based ML services, and strong leadership skills.

The predicted salary is between 63000 - 77000 £ per year.

hackajob is collaborating with Kingfisher to connect them with exceptional professionals for this role. We’re Kingfisher, a team made up of over 74,000 passionate people who bring Kingfisher - and all our other brands: B&Q, Screwfix, Brico Depot, Castorama and Koctas - to life. We want to become the leading home improvement company and grow the largest community of home improvers in the world.

At Kingfisher, our customers come from all walks of life, and so do we. We want to ensure that all colleagues, future colleagues, and applicants to Kingfisher are treated equally regardless of age, gender, marital or civil partnership status, colour, ethnic or national origin, culture, religious belief, philosophical belief, political opinion, disability, gender identity, gender expression or sexual orientation. We are open to flexible and agile working hours and locations. Therefore, we offer colleagues a blend of working from home and our offices. Talk to us about how we can best support you!

We are looking for a Lead Machine Learning Engineer to join our Data Science team, to lead the research and development process of ML/AI services developed in the Group Data Science team. You will trailblaze the development of data science algorithms, while building, leading, nurturing and retaining a high performing data science team working on banner as well as group priorities.

  • Lead the implementation of data science projects and data science approaches to support commercial goals.
  • Develop a highly proficient team of Machine Learning Engineers, establishing collaborative ways of working.
  • Collaborate with tech, product and data teams to develop the data platforms that allow us to apply data science and embed the use of data science directly in our products and processes.
  • Support diverse teams in translating between business and data in the design of project work, and in the synthesis and communication of recommendations and results.
  • Be a champion and role model for the application of data science across the Kingfisher group.
  • Support the data leadership team in developing a “data culture” and demonstrating the value of data in our decision making.
  • Lead our efforts to develop the data science (and broader customer analytics) “brand” at Kingfisher for both internal and external audiences.

Proven experience delivering high-quality AI-based products and productionisation of Machine Learning based products. Proven experience developing cloud-based machine learning services using one or more cloud providers (preferably GCP). Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.). Strong knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib). Strong software development skills (Python is the preferred language). Proven experience in deploying ML/AI services using Kubernetes & KubeFlow. Strong management and leadership skills – previous experience managing a team. Strong influencing, communication and stakeholder management skills.

Be Customer Focused – constantly improving our customers’ experience. We listen to our customers and colleagues. We innovate products and experiences to stay ahead.

Be Human – leading with purpose, humanity and care. We do the right thing. We invest in our people and build great teams.

Be Curious – thrive on learning, thinking beyond the obvious. We focus externally, globally and build the long term. We experiment and share our learnings.

Be Agile – building trust and empowering people to work with agility. We act with pace, not perfection, role modelling 80/20. We take risks, fail fast and adapt quickly.

Be Inclusive – inspiring diverse teams to achieve together. We celebrate difference as a strength. We collaborate, breaking down silos.

Be Accountable – owning the plan, delivering results and growth. We focus on performance outcomes. We prioritise and simplify for others.

At Kingfisher, we value the perspectives that any new team members bring, and we want to hear from you. We encourage you to apply for one of our roles even if you do not feel you meet 100% of the requirements. In return, we offer an inclusive environment, where what you can achieve is limited only by your imagination! We encourage new ideas, actively support experimentation, and strive to build an environment where everyone can be their best self. We also offer a competitive benefits package and plenty of opportunities to stretch and grow your career.

Lead Machine Learning Engineer employer: Stryker Corporation

Stryker Corporation is an exceptional employer that fosters a dynamic and inclusive work culture, offering employees the chance to thrive in a remote setting while being part of a leading medical communications team. With a strong emphasis on professional development and growth opportunities, employees are encouraged to enhance their skills and advance their careers, all while contributing to meaningful projects that impact healthcare globally.

Stryker Corporation

Contact Details:

Stryker Corporation Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead 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 Stryker Corporation!

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 Lead Machine Learning Engineer at Stryker Corporation.

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 Stryker Corporation.

Apply Directly through Our Website

When you find a suitable opening like Lead Machine Learning Engineer at Stryker Corporation, 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 Lead Machine Learning Engineer

Machine Learning
AI-based Product Development
Cloud-based Machine Learning Services
GCP (Google Cloud Platform)
Classical Machine Learning Algorithms
Deep Learning Algorithms
SQL

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 Stryker Corporation, 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 Stryker Corporation. 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 Stryker Corporation

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 Stryker Corporation!

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.