AWS Outcome Driven Engineering (ODE) is a new AWS engineering organization chartered to build new AWS products by applying Amazon’s innovation mechanisms along with AWS digital technologies to real world industry problems. We dive deep with industry leaders to solve problems and unblock industries, enabling them to capitalize on new digital business models. Simply put, our goal is to use the skill and scale of AWS to make the benefits of a connected world achievable for all businesses. Our team is focused on saving hundreds of millions of dollars using cutting edge science, machine learning, and scalable distributed software on the Cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply.
We are looking for an experienced, passionate, hardworking and analytical researcher to work with our partners and build new AWS products. As a Data Scientist on the Outcome Development Engineering team, you will collaborate directly with economists and statisticians to produce modeling solutions, you will partner with software developers and data engineers to build end-to-end data pipelines and production code, and you will have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, automate and scale the analysis, and develop metrics that will enable us to continually delight our customers worldwide. As a successful data scientist, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.
A day in the life
As a Sr. Data Scientist, you will solve real world problems by analyzing large amounts of business data, defining new metrics and business cases, designing simulations and experiments, creating ML models, and collaborating with teammates in business, software, and research. The successful candidate will have a strong quantitative background and can thrive in an environment that leverages statistics, machine learning, operations research, econometrics, and business analysis.
· Working with product managers, software engineers, data engineers, other data scientists, applied scientists to design, develop, and evaluate highly innovative statistics and ML models to drive efficiency through demand sensing, inventory optimization and the design of new policies and incentives.
· Guide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
· Proactively seek to identify business opportunities and insights and provide solutions to automate and optimize key business processes and policies based on a broad and deep knowledge of data, industry best-practices, and work done by other teams.
· Collaborating with our dedicated software team to create production implementations for large-scale data analysis and/or ML models.
· Developing and owning key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business
Inclusive Team Culture
Our team has a developed a reputation for attracting, developing, and retaining amazing talent from diverse backgrounds. Yes, we do get to build really cool services and work closely with customers, but we also think a big reason for our diversity is the inclusive and welcoming culture we try to cultivate every day. We’re looking for a new teammate who is enthusiastic, empathetic, curious, motivated, reliable, and able to work effectively with a diverse team of peers; someone who will help us amplify the positive & inclusive team culture we’ve been building.
In addition to Seattle - Palo Alto, Dallas, Atlanta, the Boston Metro area, and other East Coast locations in North America will also be given consideration.
· Master’s degree in a highly quantitative field: Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.
· 4+ years of hands-on industry experience in predictive modeling and analysis, causal inference, or multivariate statistics, as an ML engineer or data scientist role, applying various ML techniques, and deep understanding the key parameters that affect their performance.
· Strong Analytical skills – has ability to scope out business problems to be solved, start from ambiguous problem statements, identify and access relevant data, make appropriate assumptions, perform insightful analysis and draw conclusion relevant to the business problem.
· Proficient with Python and data manipulation/analysis libraries such as Scikit-learn and Pandas for analyzing and modeling data.
· Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.).
· Experience with managing large and disparate data sources
· Excellent communication skills. Proven ability to communicate verbally and in writing to technical peers and business teams, educating them about our systems, as well as sharing insights and data-driven recommendations
· A PhD degree in a highly quantitative field (Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.).
· 8+ years’ experience in a ML or Data Scientist role with a large technology company.
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, information retrieval.
· Skilled with Java, C++, or other similar programming language.
· Functional knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
· Advanced knowledge and expertise with Data modeling skills, Advanced SQL with Oracle, MySQL, Redshift and Columnar Databases
· Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
· Track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment