Interested in driving thought leadership in building machine learning solutions that will derive actionable insights about the complex economy of Amazon's retail business? Whether you're passionate about building highly scalable and reliable systems or a scientist who likes to solve novel business problems, the Economic Technology team is the place for you. EconTech uses Machine Learning, Reinforcement Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon's retail business. We also develop statistical models and algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building disruptive solutions using cutting-edge technology to solve some of the toughest business problems at Amazon.
You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, define the science vision and translate it into specific plans for applied scientists, as well as engineering and product teams . You will partner with scientists, economists, and engineers on the design, development, testing, and deployment of scalable ML and econometric models while building tools to help our customers gain and apply insights. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. This role combines science leadership, organizational ability, technical strength, product focus and business understanding.
As an Applied Scientist, you bring structure to ambiguous business problems and use science, logic, and practical experience to decompose them into straightforward, scalable solutions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems; you're interested in learning; and you acquire skills and expertise as needed. This individual will be able to work equally well with Science, Engineering, Economics and business teams. This person will have sound judgment and help recruit and groom high caliber science talent.
• Drive collaborative research and creative problem solving
• Constructively critique peer research and mentor junior scientists and engineers
• Create experiments and prototype implementations of new learning algorithms and prediction techniques
• Collaborate with engineering teams to design and implement software solutions for science problem
• Contribute to progress of the Amazon and broader research communities by producing publicationsBASIC QUALIFICATIONS
• PhD degree with 4+ years of applied research experience or a Master's degree and 6+ years of applied research experience
• 3+ years of experience in building machine learning models for business application
• Experience with machine learning, data mining, and/or statistical analysis tools
• Significant hands-on experience with at least two programming languages, such as Python, Scala, Java, C# or similar languages
• Excellent communication, writing and presentation skillsPREFERRED QUALIFICATIONS
• Ph.D. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
• 6+ years of practical experience applying ML to solve complex problems in an applied environment
• Significant peer-reviewed scientific contributions in premier journals and conferences with high quality citations/h index
• Strong CS fundamentals in data structures, problem solving, algorithm design and complexity
• Strength in clarifying and formalizing complex problems
• Ability to convey mathematical results to non-science stakeholders
• Experience with defining research and development practices in an applied environment
• Proven track record in technically leading and mentoring scientists
• Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts