Overview:
We're Kingfisher, A team made up of over 76,000 passionate people who bring Kingfisher - and all our other brands: B&Q, Screwfix, Brico Depot, Castorama and Koctas - to life. That's right, we're big, but we have ambitions to become even bigger and even better. We want to become the leading home improvement company and grow the largest community of home improvers in the world. And that's where you come in.
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, both of hours and location. Therefore, we offer colleagues a blend of working from home and our offices, located in London, Southampton & Yeovil. Talk to us about how we can best support you!
We are looking for a Senior Machine Learning Engineer to join our growing team, to develop and deploy core ML/AI algorithms required to tackle data science challenges across Kingfisher Group. You will support data science projects from start to production, developing quality code and carrying out automated build and deployments, working closely with colleagues in the Data Science team as well as stakeholders across the business.
What's the job?:
Develop high-quality machine learning models to solve business challenges
Develop production quality code and carry out basic automated builds and deployments
Write comprehensive, well written documentation that meets our needs
Identify work and dependencies, tracking progress through a set of tasks
Communicate clearly with colleagues and business stakeholders
Proactively share ideas with colleagues and accept suggestions
Ability to work on multiple data science projects and manage deliverables
What you'll bring:
Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture
Solid understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc), state-of-the-art research area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc)
Solid knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib, etc)
Understanding of model evaluation, data pre-processing techniques, such as standardisation, normalisation, and handling missing data
Solid understanding of summary, robust, and nonparametric statistics; hypothesis testing, probability distributions, sampling techniques, and stochastic processes
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