SCOT's Regional team in Japan is seeking a passionate and driven Senior Applied Scientist to lead localization efforts around substitution science with a primary focus on improving customer experience in a sustainable manner. This would include adapting WW datasets incorporating country specific requirements (e.g. Japanese language, translations) to model basket awareness and speed sensitivity of large cohorts of products.
You will work with product managers, BIEs, and business teams to understand the business problems and requirements, evaluate existing science models and distill that understanding to crisply define durable yet frugal improvements to address local challenges. You will tackle complex technical challenges that require extensible solutions while collaborating with multiple teams across EU, IN and Emerging Countries to assess overall opportunity for customers and design experiments to benchmark/baseline impact.
As a Senior Applied Scientist, you will create and implement sophisticated machine learning solutions while creating technical strategies with minimal supervision. The ideal candidate possesses extensive knowledge and hands-on experience, particularly in areas such as deep learning systems, computer vision, or natural language processing. You should demonstrate superior analytical and quantitative skills, with proven experience in data collection, model building, testing and validation. Proficiency in programming languages like Python, R, or Matlab, along with expertise in machine learning frameworks such as PyTorch, TensorFlow, etc. is essential.
We value professionals who can earn trust through strong communication with stakeholders, understand business requirements, and deliver solutions that address specific needs. You should have the ability to think strategically and connect scientific solutions to create significant impact for customers. The role requires mentoring junior colleagues, contributing to the career development of team members, and fostering a collaborative environment.
Success in this position demands a self-driven desire to learn new domains, strong ownership mentality, and the ability to deliver results while maintaining high standards. Experience with AWS or other cloud technologies and distributed systems would be advantageous.
Key job responsibilities
- Design and implement novel scientific solutions while working backwards from customer needs and product requirements to deliver measurable business impact
- Lead complex cross-functional projects and ensure high-quality solutions by collaborating with solutions architects, developers, product managers, and senior leadership
- Create technical strategies and roadmaps for forward-looking research, communicating them effectively to senior leadership
- Develop and implement scalable algorithms and solutions that are extensible for future needs
- Conduct hands-on experimentation and deliver results in the form of new products while maintaining strong analytical and quantitative standards
- Research and benchmark technology solutions against competing systems in the industry
- Exert technical influence across multiple teams by sharing deep knowledge and experience to increase productivity and effectiveness
- Communicate technical concepts and solutions appropriately for both technical and non-technical audiences while earning trust of business decision-makers
About the team
Have you ever ordered a product on Amazon and when that box with the smile arrived, wondered how it got to you so fast? Wondered where it came from and how much it cost Amazon? If so, Amazon’s Supply Chain Optimization Technology (SCOT) organization is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment working with smart & passionate team members. (Learn more about SCOT: http://bit.ly/amazon-scot)- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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