Lead Data Scientist: Python, Azure, Machine Learning, Spark

Location:
London, Greater London, England
Job Type:
Contract
Industry:
Cloud & Infrastructure
Job reference:
BBBH140071_1623945248
Posted:
about 1 month ago

Lead Data Scientist: Python, Azure, Machine Learning, Spark


Lead Data Scientist: Python, Azure, Machine Learning, Spark

The work location is in Paddington, London (remote working) and is a 6 month contract.

The pay rate on offer is £650 per day.


The role is working for a large multinational retail client.

About the Project:

There are 3 Lead Data Science Positions working on exciting projects that consist of:

  • Customer Loyalty
  • Price Modelling/ Marketing
  • Consumer/Customer Data Sets

Experience of the above domains is highly advantageous for this role

Key skills

  • Significant experience in landing data science capability, applying the most effective statistical /machine learning models on real world commercial problems and having measured the business benefits
  • MSc or PhD in a STEM subject e.g. Computer Science, Statistics, Mathematics, etc.
  • Strong statistical background applied across a number of areas; segmentations, NLP, predictive modelling, recommendation systems. Experience using both simple and complex statistical models such as; regression, clustering, affinity analysis, causal inference models, time series, convolutional neural networks, transformers.
  • Comprehensive proficiency in key programming and scripting languages (e.g. Python, R, SPARK, SQL, etc) and software development skills.
  • Expert in mining large & complex data sets - both structured and unstructured data and including (but not limited to) efficient extraction of data, transformation and application
  • Able to demonstrate innovation in approach and application
  • Development of collaborative relationships with colleagues across the business
  • Clear communication skills are essential as the role will require translating data science into actionable insight and influencing at different levels within the business

Key accountabilities and measures

  • Use expert knowledge of data science techniques and statistics to both lead and regularly deliver complex projects into production, with a robust commercial approach, being mindful that operationalisation is a key success criteria
  • Implement a highly visual and commercial approach when delivering data science projects that engages and challenges the thinking of non-technical audiences
  • Build industrialized data science products by promoting correct software development standards and practices
  • Work with business and technology partners to establish a productive analytical and development environment
  • Work as a self-starter and hands-on technical expert, take the ownership of shipping code into production, and play a technical role model

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