Senior Machine Learning Scientist (For independent contractors)

The Senior Machine Learning Scientist is a deep subject-matter expert in the theory behind one or more areas of machine intelligence (e.g. recommenders, optimization, NLP, Computer Vision) and its implementation as an end-to-end product that generates direct business impact. They define the strategy and vision for how to generate outsized impact through automated intelligence in a broad product area by driving a research agenda and development plan from ideation to prototyping to full productionisation.

Responsibilities:

• Translate broad business problems into ML/AI challenges and develop a targeted research plan to identify the best approach within the constraints of the production environment.

  
  

• Develop the strategy for machine intelligence on a product family by designing innovative ML/AI models, algorithms, and approaches that deliver both short-term commercial impact and longer-term differentiated business value and customer experiences.

  
  

• Independently define and build proof-of-concepts to test new ideas and demonstrate their potential value to relevant stakeholders.

  
  

• Drive the end-to-end execution of the ML/AI development process on products, from understanding product requirements, data discovery, model development and evaluation, to implementation of a full production pipeline for both batch- and stream-based deployment.

  
  

• Develop production-grade ML code for models, features, and pipelines, accounting for scalability, latency, realtime requirements, monitoring and retraining.

  
  

• Build readable and reusable code, using the right technologies and coding methodologies applying knowledge of business area tools and product needs.

  
  

• Create outsized impact for your work through carefully designed rapid prototyping coupled with strategic scale-up in production through the work of other teams.

  
  

• Promote platform-based development and reuse by coaching teams in abstracting individual business problems to generalized ML/AI products, rather than point solutions, and in identifying horizontal opportunities across multiple business domains.

  
  

• Maintain a highly cross-disciplinary perspective, solving issues by applying approaches and methods from across a variety of ML/AI disciplines and related fields.

  
  

• Coach others through evidence and clear communication, explaining advanced technical concepts in simpler terms.

  
  

• Continuously evolve their craft by keeping up to date with the latest developments in ML/AI and related technologies and upskilling on these, as needed.

  
  

• Responsible for data management related Data Governance processes as defined in the Data Governance Framework (E.g. monitoring of data quality, management of data lineage, and maintenance of logical data model).