Senior Manager, Data Science

Key Skills

A/B Testing, Java Programming, MATLAB Programming, Python Programming, R Programming, SAS, SQL

Job Description

Job summary

Are you passionate about leveraging your data science and machine learning skills to make an impact at scale? Do you enjoy developing innovative algorithms, optimization and predictive models to generate recommendations that will be used by automated systems to drive hundreds of millions of impact on Amazon Retail's cash flow? If these questions get you excited, we definitely want to hear from you.

Strategic Sourcing team, as part of Amazon Supply Chain Optimization and Technology organization, is seeking an experienced and motivated Data Science leader. Strategic Sourcing team owns systems that are designed to: 1) reduce end to end costs from inbound supply chain and (2) improve vendor performance. Some of the key decisions that these systems make: when and if we should source a product (e.g. is the product obsolete or temporarily unavailable); from which vendor and at what cost we should source an ASIN; what is the ideal supply chain setup (e.g. Pallet, Truckload, Vendor Initiated PO, etc.) for an ASIN/vendor; when should vendor ship/deliver inventory to Amazon FCs; which inbound lanes – vendor warehouse to Amazon FC – should have pre-allocated transportation with how many shipments; when should we penalize vendors for defects/infractions through chargebacks and by how much. Together these set of decisions and systems work together to ensure Amazon’s inventory needs are met on time and in the most efficient way. We develop sophisticated algorithms that involve learning from large amounts of data from diverse sources such Vendors, Transportation carriers, Amazon warehouses etc.

Key job responsibilities

• As the Data Science Manager on this team, you will:
• Lead of team of scientists on solving science problems with a high degree of complexity and ambiguity
• Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects
• Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management
• Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization
• Analyze historical data to identify trends and support optimal decision making
• Apply statistical and machine learning knowledge to specific business problems and data
• Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed

BASIC QUALIFICATIONS

• 5+ years of hands-on experience as a scientist or science manager in building quantitative solutions
• 2+ years of experience managing teams of Machine Learning, Data Science and/or Engineering professionals
• Ph.D. or Masters in Computer Science, Machine Learning, Statistics or a related quantitative field
• Expertise in as many of the following: hypothesis testing, estimation, experimental design, hypothesis and A/B testing, causal inferencing, multi-variate testing & design, descriptive analytics, and regression analysis.
• Experience with data scripting languages (e.g. SQL, Python, R) or statistical/mathematical software (e.g. R, SAS, or Matlab)

PREFERRED QUALIFICATIONS

• Broad knowledge of ML methods, statistical analysis, and problem-solving skills.
• Expert level knowledge in statistics; sophisticated user of statistical tools.
• Experience processing, filtering, and presenting large quantities (hundreds of millions/billions of rows) of data
• Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization.
• Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
• Excellent verbal and written communication skills with the ability to advocate technical solutions for science, engineering, and business audiences.
• Ability to develop experimental and analytical plans for data modeling, use effective baselines, and accurately determine cause-and-effect relations.accurately determine cause-and-effect relations.