The Amazon PeopleInsight is the behind-the-scenes team that enables our Operations and Human Resource Leaders to make informed decisions. The team builds reporting and analytics tools that fulfill customer promise every day. You will be part of PeopleInsight Science Team. We provide end-to-end ML solutions. We solve complex problem using science, supervised and un-supervised ML models and statistical analysis. We build scalable production pipelines, deploy our products to platforms which support WW Consumer.
A day in the life Your day-to day activity will be working on complex science products, develop solutions, provide science review of models for production, and write research papers, work closely with data engineers to help scaling data pipeline for customized models, work with SDEs and QA teams to deploy ML models into production, present your work to product team and senior leadership.
About the hiring group We are a diverse team of applied scientists, data scientists and software developers We are a highly motivated individuals and work in a fast-paced development environment. We are self-starters where ALL ideas are counted. Our biggest motivation is to help our customers and create great experience for them.
Job responsibilities Amazon delights millions of customers around the world. Meet the behind-the-scenes team that enables our Operations and Human Resource Leaders to make informed decisions. The Amazon PeopleInsight team builds reporting and analytics tools for our teams that fulfill customer promise every day. Whether it is Fulfillment Center team that delivers your Prime order in two days, our Amazon Locker team that lets you pick up your package anytime that is convenient for you, our Prime Now team getting you lunch in under an hour, or one of many more, the PeopleInsight group is there providing people metrics along the employee life-cycle for our global operations businesses. In addition to standard reporting, we build products using machine learning aiming to help our leaders focus their efforts in ways that will engage, retain and grow their associates.
We are a diverse team of applied scientists, data scientists and software developers and we are looking for an experienced, results-oriented Applied Scientist, who will help to design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment. You will be a key contributor in building statistical models supporting A/B platform, building end-to-end science model pipelines. You will work with the team of software developers and machine learning experts to help them deploy products. You will also be involved in every aspect of the production pipeline process. It will include portability, scalability, operationalization and test, models deployment, create reproducible pipelines, API endpoints.
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.
· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field · 5+ years of experience producing models and analytics for business applications using statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression) · 2+ years of experience of building standard machine-learning models for business application · Experience programming in Java, C++, Python, Scala, R, or related language
· Experience with data visualization and presentation, turning complex analysis into insight · Experience with productionizing statistical models using AWS features (S3, Redshift, Sagemaker) and Scala · Excellent quantitative modeling, statistical analysis skills and problem-solving skills. Sophisticated user of statistical tools · Experience collaborating with data scientists, business intelligence or other technical roles · Excellent communication including direct correspondence with senior leadership · Demonstrable track record dealing well with ambiguity, prioritizing needs, and delivering results in an agile, dynamic environment