Data Scientist, Optimal Inventory Health, SCOT Field, Optimal Inventory Health

- 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
- Experience managing data pipelines
- Experience as a leader and mentor on a data science team

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $143,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Amazon SCOT OIH (Supply Chain Optimization Technology - Optimal Inventory Health) team owns inventory health management for Retail worldwide. We use a dynamic programming model to maximize the net present value of inventory driving actions such as pricing markdowns, deals, removals, coupons etc.
Our team, the OIH Insights Team energize and empower OIH business with the clarity and conviction required to make impactful business decisions through the generation of actionable and explainable insights, we do so through the following mechanisms:
-- Transforming raw, complex datasets into intuitive, and actionable insights that impact OIH strategy and accelerate business decision making.
-- Building and maintaining modular, scalable data models that provide the generality, flexibility, intuitiveness, and responsiveness required for seamless self-service insights.
-- Generating deeper insights that drive competitive advantage using statistical modeling and machine learning.

As a data scientist in the team, you can contribute to each layers of a data solution – you work closely with business intelligence engineers and product managers to obtain relevant datasets and prototype predictive analytic models, you team up with data engineers and software development engineers to implement data pipeline to productionize your models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality.
You will be diving deep in our data and have a strong bias for action to quickly produce high quality data analyses with clear findings and recommendations. The ideal candidate is self-motivated, has experience in applying technical knowledge to a business context, can turn ambiguous business questions into clearly defined problems, can effectively collaborate with research scientists, software development engineers, and product managers, and deliver results that meet high standards of data quality, security, and privacy.

Key job responsibilities
1. Define and conduct experiments to optimize Long Term Free Cash Flow for Amazon Retail inventory, and communicate insights and recommendations to product, engineering, and business teams
2. Interview stakeholders to gather business requirements and translate them into concrete requirement for data science projects
3. Build models that forecast growth and incorporate inputs from product, engineering, finance and marketing partners
4. Apply data science techniques to automatically identify trends, patterns, and frictions of product life cycle, seasonality, etc
5. Work with data engineers and software development engineers to deploy models and experiments to production
6. Identify and recommend opportunities to automate systems, tools, and processes