Amazon Worldwide Operations’ (WWOps) Talent Strategy team guides the talent strategy for WWOps and invents solutions that improve mean levels of performance for the largest workforce within Amazon, one of the fastest growing companies in the world. Talent Strategy develops science-based processes and tools, identifies and develops high-potential talent to fill future organizational needs, onboards new employees at scale, and provides lifecycle leadership and functional learning solutions. WWOps is comprised of hundreds of thousands of Amazon employees across multiple global order fulfillment, transportation, corporate, and customer service organizations.
As a data scientist supporting our behavioral nudging and change initiatives, you will ask research questions about the theory and application of behavioral nudging, develop novel approaches to answering those questions, and deploy systems that use those models to deliver the right nudge at the time to the right person. Because our behavioral nudging work supports employees around the world, you will have the opportunity to work with scores of large datasets in more than a dozen languages.
How do we help employees find their next role at Amazon? How do we make sure promotions happen at the point of readiness? How we do ensure job recommendations are inclusive and unbiased? How do we support each employee in their efforts for a fulfilling career? How do we help new employees build professional networks? How do we balance all of these opportunities, and provide more personalized support without being noisy, needy, or creepy?
Together with a multi-disciplinary team of scientists, engineers, and product managers you will explore the answers to these questions and build solutions to act on those answers. Along the way, you will have the opportunity to tackle technical and scientific challenges, participate in the Amazon AI community, mentor scientists and engineers with an interest in NLP/ML/DL, influence the future of behavioral nudging at Amazon, and positively impact hundreds of thousands of employees around the world.
The ideal candidate for this role will be a collaborative team player with outstanding technical abilities, communication skills, project management experience, and start-up mentality. You will have hands-on experience leading product development initiatives, and are able to balance technical leadership with strong business judgment to make the right decisions about technology, models, and methodologies choices. You strive for simplicity, and demonstrate significant creativity and high judgment backed by statistical proof.
· Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.).
· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
· Directly contribute to the design and development of automated forecasting systems.
· Build customer-facing reporting tools to provide insights and metrics which track forecast performance and explain variance.
· Collaborate with researchers, software developers, and business leaders to define product requirements, provide analytical support, and communicate feedback.
· Presenting critical data in a format that is immediately useful to answer questions about the inputs and outputs of Forecasting systems and improving their performance.
Amazon.com is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age.
· 6+ years of work experience in an analytical role involving data extraction, analysis, and communication.
· Experience building complex data visualizations.
· Experience working in command-line Linux environments.
· Experience with causal inference, applied time series modeling or machine learning forecasting applications.
· Strong project management skills.
· Masters/PhD in quantitative field
· Expertise in machine learning techniques (Supervised & Unsupervised) such as predictive modeling, clustering, recommendation systems, bandit methods, reinforcement learning, anomaly detection, tree-based methods
· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity, prioritizing needs, and delivering results in an agile, dynamic environment.
· Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
· Excellent written and verbal communication skills. The role requires effective communication with colleagues from research, human resources and business backgrounds.
Ideal candidate profile
· MS in a quantitative discipline such as Statistics, Mathematics, Physics, Engineering, Computer Science or Economics.
· 4+ years work experience in an analytical role involving data extraction, analysis, and communication.
· Proficiency in at least one statistical software package such as R, Stata, Matlab, or Python.
· Experience with object-oriented programming languages.
· Expertise using SQL for acquiring and transforming data.
· Outstanding quantitative modeling and statistical analysis skills.
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences.