Senior Applied Scientist, Jp Science and Data - Avs Proserve

Amazon Amazon · Big Tech · 13, Japan +1 · Machine Learning Science

Senior Applied Scientist role focused on developing and applying machine learning algorithms to solve business problems for vendors, enabling them to understand customers and identify growth opportunities. The role involves building orchestrated ML solutions, translating model outputs into actionable insights, measuring business impact, and leading cross-functional collaboration.

What you'd actually do

  1. Develop customer understanding models by designing and building orchestrated ML solutions that enable vendors to deeply understand their customers and uncover growth opportunities
  2. Bridge science and business strategy by translating model outputs into actionable insights and recommendations that inform vendor growth strategies, customer acquisition, and long-term planning
  3. Measure and validate business impact by closing the loop between science solutions and business outcomes — establishing measurement frameworks that quantify impact, surface new opportunities, and continuously refine the path to vendor growth
  4. Lead cross-functional collaboration by working with engineers, scientists, consultants, and business leaders to deploy scalable solutions while communicating complex technical concepts clearly to non-technical audiences
  5. Stay at the forefront of innovation by applying state-of-the-art techniques in machine learning, interpretable models, and transformers to solve ambiguous business problems while fostering rapid experimentation and continuous learning

Skills

Required

  • building machine learning models for business application
  • PhD or Master's degree and 6+ years of applied research experience
  • programming in Java, C++, Python or related language

Nice to have

  • modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • large scale distributed systems such as Hadoop, Spark etc.
  • statistical modeling
  • machine learning
  • transformer architectures
  • representation learning
  • designing experimentation and measurement frameworks to evaluate model performance and business impact

What the JD emphasized

  • ambiguous business problems
  • deep expertise in machine learning, interpretable models, transformers, and experimentation
  • business acumen to translate problems into scalable science solutions
  • self-starter with an entrepreneurial spirit who is comfortable with ambiguity
  • strong attention to detail
  • thrives in a fast-paced, data-driven environment
  • passion for driving measurable impact
  • designing and building orchestrated ML solutions
  • measure and validate business impact
  • establishing measurement frameworks that quantify impact
  • communicating complex technical concepts clearly to non-technical audiences
  • applying state-of-the-art techniques in machine learning, interpretable models, and transformers
  • fostering rapid experimentation and continuous learning
  • 3+ years of building machine learning models for business application experience
  • Experience with designing experimentation and measurement frameworks to evaluate model performance and business impact

Other signals

  • develop and apply machine learning algorithms
  • customer understanding models
  • orchestrated ML solutions
  • measure and validate business impact
  • deploy scalable solutions