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Position: Location: Salary: Type:
Permanent
 
Working with an enterprise software company who deal with Accounting Softwares. About 10,000 accountants are running with the software accounting to over a million companies. The London branch using AI to build tools to automate accounting.
Looking for a Data Scientist with experience of developing and deploying machine learning models at scale.
 
Essential:
  • Formal training in a numerate discipline (statistics, mathematics, physics, data science, etc.)
  • Good understanding of machine learning principles (experimental design, statistical tests, machine learning algorithms, cross-validation, working with unbalanced / non-IID datasets)
  • Confident Python programmer (pandas, numpy, scikit-learn, some understanding of web APIs, e.g. Flask) who writes clean, well-structured code
  • Good understanding of data structures, and pros / cons of different data structures
  • Good understanding of SQL
  • Some experience of containerised deployment of ML services (e.g. Docker, Kubernetes)
  • Creative thinker
  • Good at communicating ideas to non-specialists
  • Team player
 
Desirable:
  • Experience with Linux servers in virtualised environments
  • Experience of developing complex, end-to-end ML train/test/deploy pipelines
  • Experience of working with NoSQL databases, especially MongoDB or other JSON-like structures
  • Understanding of other programming languages besides Python (especially Scala, node.js, Go, C++)
  • Contributor to open source projects
 
Non Negotiables:
  • Formal training in a numerate discipline (statistics, mathematics, physics, data science, etc.)
  • Good understanding of machine learning principles (experimental design, statistical tests, machine learning algorithms, cross-validation, working with unbalanced / non-IID datasets)
  • Python programmer (pandas, numpy, scikit-learn, some understanding of web APIs, e.g. Flask) who writes clean, well-structured code
  • Good understanding of data structures, and pros / cons of different data structures
  • SQL
  • Some experience of containerised deployment of ML services (e.g. Docker, Kubernetes)
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