Jobs for People with MS: National MS Society

Description

SCOT's Regional team in Japan is seeking an experienced and motivated Data Scientist to help optimize, enhance, and scale our selection decision algorithms. The focus will be on improving coverage of automated profitability tools by building models that address nuances (like frequent deal events, country specific gaps), running experiments to validate effectiveness, and informing long term roadmap choices.

You will work closely with Product Managers, BIEs, Supply Chain Managers, and other Scientists across US, EU, and Japan to build scalable, high insight- high impact products and own improvements to business outcomes within your area, enabling execution of WW and local solutions for next generation selection/assortment systems.

You will leverage your expertise in modeling, maths and algorithms to analyze diverse and large datasets to uncover insights that help minimize dependence on manual assortment decisions. The analyses could include: 1) creating decision frameworks to choose between multiple options or cohorts of selection to surface to our customers; 2) creating segmentation frameworks or identify groups or clusters that behave similarly to each other; or 3) building statistical or predictive models to calculate the expected values of outcomes, eventually integrating with the central tech solutions owned by SCOT WW

Key job responsibilities

You will be responsible for researching, experimenting and analyzing predictive and optimization models. You will work on ambiguous and complex business and research science problems with large opportunities. You are an analytical individual who is comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams.

Key job responsibilities

• Interact with product managers to understand their business requirements and operational processes

• Frame business problems into scalable solutions

• Adapt existing and invent new techniques for solutions

• Gather data required for analysis and model building

• Collaborate with product and engineering teams to design rigorous experiments to evaluate the impact of new features or algorithms

• Create and track accuracy and performance metrics

• Prototype models by using high-level modeling languages such as R or in software languages such as Python.

• Familiarity with transforming prototypes to production is preferred.

• Create, enhance, and maintain technical documentation

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)

Basic Qualifications

• 2+ years of data scientist experience

• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

• Experience applying theoretical models in an applied environment

Preferred Qualifications

• Experience in Python, Perl, or another scripting language

• Experience in a ML or data scientist role with a large technology company

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.