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Data Scientist

  • Location:


  • Sector:

    Data and Analytics

  • Job type:


  • Job functions:

    Data Scientist

  • Salary:

    Up to £55000 per annum

  • Contact:

    Jemma Challenger

  • Contact email:

  • Job ref:


  • Published:

    11 days ago

  • Expiry date:


  • Startdate:


La Fosse Associates are currently partnering with a leading international home-improvement retail company with over 1,300 stores, supported by a team of over 79,000 colleagues.

The company is looking to bring on a talented Data Scientist to help develop and deploy core ML/AI algorithms required to tackle key data science challenges within the business in line with its ambitious growth plans.

Reporting into the Lead Data Scientist, the successful candidate will:

  • Develop high-quality machine learning models to solve business challenges.
  • Develop production quality code and support data science projects from start to production.
  • Understand and be able to apply most standard methodologies and principles.
  • Carry out basic automated builds and deployments.
  • Write comprehensive, well written documentation that meets our needs.
  • Identify work and dependencies and supervise progress through a set of tasks.
  • Proactively share ideas with colleagues and accept suggestions.
  • Engage in dialogue with partners to meet their request.
  • Work within and provide support to the rest of data science team.
  • Work on multiple data science projects and run results.


  • 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.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and 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.
  • Solid communications and soft skills to manage stakeholders demands.