Machine Learning Engineer
- 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
- 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
- I listen to my customers
- I use available data to help make decisions
- I do the right thing
- I am respectful
- I build and share new ideas
- I try new things and share my learnings
- I have courage to be creative
- Done is better than perfect, I aim for 80/20
- I embrace allyship
- I have self-awareness and a desire to learn
- I own my actions
- I understand the Kingfisher plan and how it relates to my role
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