Product Machine Learning Engineering Manager

Added: 15/01/2021

REF: 6989

Contract: Permanent

Location: Boston, Massachusetts, United States

As the Product ML Manager, you

  • Report directly to the COO 
  • Lead our product machine learning efforts in a number of key areas of our platform.
  • Have extensive experience working in distributed organizations as part of the technology team
  • Have a track record of successfully developing scalable ML models and turning these into actionable product insights
  • Have extensive technical skills and are up to date with the latest developments in the ML field being proficient in supervised, unsupervised, and reinforcement learning methods
  • Have mastery of all areas of machine learning and quantitative analysis including hypothesis generation, model selection, model development, training and validation, inference, scalability and production deployment of large-scale models, exploratory analysis, and data visualization
  • Are quantitative and systematic in all areas of your work
  • Are judicious in developing and applying the simplest statistical methods that will get the job done, instead of reaching for fashionable and unnecessarily complex
  • Are able to communicate effectively using data at all levels of the organization  
  • Are passionate about making a difference in the lives of patients.

Background Requirements:

  • Preferably a PhD but at least a Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a similar field, or equivalent professional experience
  • Extremely solid fundamentals in statistical learning methods
  • 7 years' experience in ML engineering
  • 2 years' experience as a direct people manager
  • Deep experience with multiple ML stacks, such as Keras, Pytorch, Tensorflow, scikit-learn
  • Expertise in exploratory analysis and data visualization
  • Expertise in building and supporting large-scale production ML models through multiple iterations.
  • Well-rounded software engineering experience, including a variety of technology ecosystems such as Python, Java, and C++.
  • Experience with genomics, digital image analysis, and clinical data analysis is an asset
  • Excellent interpersonal and communication skills
  • Knowledge of software engineering best practices (Agile, Continuous Value Delivery, CI/CD, DevOps, NoOps, PaaS, IaaS, LEAN software, Service Oriented Architecture, cloud computing)


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