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Data Scientist @ Boston, MA at VBeyond (Boston, MA)

Job Description

You will contribute to our large-scale, real-time machine learning ecosystem that employs a range of collaborative filtering, learning-to-rank, deep learning and NLP techniques to:
  • Serve personalized recommendations at various shopping stages and channels
  • Show relevant search results
  • Enable Client ways to do visual fashion discovery
  • Optimize the content and style of webpages

Our technology stack is based on Spark Client, Tensorflow, PyTorch and custom python and scala models. Our models are trained one of the largest fashion e-commerce datasets in the world and deployed on our cloud machine learning platform. Every engineer has its own customizable Jupyter Lab environment.

On the short term we are looking for machine learning engineers with the following profiles:

Computer Vision Client Engineer
Responsibilities:
  • Develop new computer vision models optimized for visual similarity and attribution
  • Build image scraping, training and attribution pipelines
  • Contribute to building a robust deployment infrastructure

Experience level:
  • Recent M.Sc. or PhD Graduates

Requirements:
  • You are familiar with several deep learning architectures and loss functions and know how to fine-tune on custom datasets
  • You know how to debug computer vision deep learning pipelines
  • You know how to analyze deep learning pipelines and propose improvements
  • You are familiar with embeddings and know how to evaluate and use embedding spaces
  • You are familiar with Tensorflow, PyTorch or Keras

Recommendation Client Engineer
Responsibilities:
  • Evaluate & implement improvements to our existing recommendation engines
  • Evaluate performance of recommendation engines for Client use cases
  • Support and debug production issues

Experience level:
  • M.Sc. with 1-2 years experience
  • B.Sc. with several years of experience and extensive course work
  • Practical experience with machine learning models

Requirements:
  • You are familiar with one or more collaborative filtering algorithms like item-item, co-occurrence or matrix factorization
  • You know how to evaluate recommendation algorithms in both offline and online experiments
  • You have practical experience building real-time low-latency machine learning algorithms
  • You know how to build and optimize Spark pipelines
  • You can write production-level Python code

Search relevance Client engineer
Responsibilities:
  • Build search relevance algorithms and integrate them into the core search engine
  • Build data pipelines for our feature store
  • Propose and evaluate optimization metrics

Experience level:
  • M.Sc. / PhD with 1-2 years of experience
  • B.Sc. with several years of experience and extensive course work
  • Practical experience in relevant industry

Requirements:
  • You are familiar with one or more ranking algorithms and other concepts to boost search relevance
  • You have practical expertise in several disciplines of information retrieval, machine learning and natural language processing, topic modeling and ranking and search relevance
  • You have experience building low-latency machine learning directly into the core search engine.
  • You know how to evaluate ranking algorithms in both offline and online experiments