Sr. Data Scientist
At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We are looking for a Sr. Applied/Data Scientist to be a part of the consumer Recruiting Analytics & Forecasting team who will build models and algorithms for demand forecasting and capacity planning ground up whereby enabling the hiring of exceptional talent meeting the Amazon bar.
Our success in recruiting depends on our ability to manage and analyze the data that that we generate as well as curating external data sources for identifying the right resources for Amazon. This position requires the ability to dive deep large amounts of data, have a great business sense, and the desire to influence key strategic decisions with data-modelled analysis. Working within the business teams and collaborating with key stakeholders across the company, you will have the opportunity to design and implement features to enhance the experience of Amazon Recruiters and prospective Employees. We offer a technologically-sophisticated, customer-focused, data-driven and friendly work environment. Sr. Applied/Data Scientists at Amazon work directly with diverse scientific teams including Computer Engineers, Data Engineers and other scientists. In this role, you will have an opportunity to work on a mathematical problem, with a large element of unpredictability. You will analyze and process large amounts of data, develop new sophisticated algorithms and improve existing approaches based on statistical models, machine learning algorithms to generate demand plans based on distributions of confidence.
As a Sr. Applied/Data Scientist in Amazon recruiting you will partner with business, technology and recruiting leaders to identify future trends, improve turnaround time and the hiring efficiency by modelling some of the very sophisticated use-cases. If you are excited about data science, machine learning, are results oriented, and want to join a growing data analytics team within Amazon - this role is for you.
The ideal candidate will have excellent analytical abilities, outstanding business acumen and judgment, intense curiosity, strong technical skills, and superior written and verbal communication skills. He/she will have a strong bias toward data driven decision making. He/she will be a self-starter, comfortable with ambiguity, able to think big and be creative while paying careful attention to detail, and will enjoy working in a fast-paced dynamic environment.
§ Process and analyze data; gather additional data sources that would improve model accuracy
§ Build mathematical models to represent demand forecasting at various levels.
§ Prototype these models by using high-level modeling languages such as R or in software languages such as Python.
§ Create, enhance, and maintain technical documentation, and present to other scientists and business leaders.
§ PhD in Machine Learning, Statistics, Applied Mathematics or a related quantitative field and 1+ years of industry experience, or a Master's degree in the related fields with 2+ years of industry experience
§ 3+ years experience building, iterating and validating statistical models
§ Knowledge of data management, data cleaning, machine learning, and analytics techniques.
§ Fluency in R, Python or a similar modeling language and in SQL
§ Proficiency in at least one modern programming language such as Java or C++
§ Strong verbal and written communication, influencing and partnership skills
§ Ability to convey rigorous mathematical concepts and considerations to non-experts
§ Experience with large data sets
§ Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives
§ Experience designing and supporting large-scale distributed systems in a production environment
§ Previous experience in a ML or data scientist role with a technology company
§ Knowledge of scripting for automation (e.g. Python, Perl, Ruby)
§ Working knowledge of visualization tools (e.g. Tableau, Shiny, D3)
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