People & Team Management   

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  Contribute to hiring and building a great pool of Data Scientists and Engineers for your team and support the recruitment activities of other data functions  

  
  


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  Motivate, inspire, coach, mentor colleagues within the Data Science team to help them develop technical excellence  

  
  


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  Motivate, inspire, coach, mentor business partners and stakeholders to help them identify new transformational possibilities that Data Science enables  

  
  


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  Define clear objectives for each individual you manage  

  
  


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  Ensure each individual you manage has a personal development plan and regularly proactively works on it. 

  
  


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  Support and manage the future shape of the ML engineering team. 

  
  

Functional Requirements  

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  Contribute to all aspects of the Data Science Project Lifecycle, with a focus on scalable operation and productionisation. 

  
  


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  Can coordinate and manage competing priorities across a portfolio of projects  

  
  


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  Provide leadership and guidance on the development and monitoring of ongoing data engineering pipelines and the delivery of ML models from prototypes to production.  

  
  


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  Own and define the key performance indicators (KPIs) and diagnostics to measure performance against business goals 

  
  


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  Ability to influence across organisations, proven collaboration skills, comfortable working with ambiguity, ability to make quick, informed decisions taking into account trade-offs. 

  
  


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  Compile, integrate, and analyse data from multiple sources to enable the data scientists to answer business questions  

  
  


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  Can conceptualize, formulate, prototype and implement ML pipelines to capture customer behaviour and solve business problems  

  
  


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  Proven extensive experience developing and implementing Machine Learning engineering pipelines on large data sets 

  
  


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  Be an expert in data modelling and data architecture on complex data sets using SQL, Hadoop, NoSQL and Spark or other distributed computed systems. 

  
  

Domain Expertise  

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  Have a good working knowledge cloud based and local data science frameworks and toolkits.  

  
  


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  Are experienced in Agile methodologies and the hypothesis-driven approach 

  
  


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  Have a deep knowledge of a sufficiently broad area of technical specialism in ML engineering methodology and best practices and are a valued and trusted expert 

  
  


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  Have a practical experience productionising machine learning, Deep Learning and natural language understanding/processing models 

  
  


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  Experience with Cuda, pyTorch or TensorFlow and Azure