Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.
Sponsored Products help merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.
As the Data Science Manager on this team, you will:
· Lead and manage a team of scientists, business intelligence engieers (etc) on solving science problems with a high degree of complexity and ambiguity.
· Develop science roadmaps, run annual planning, and foster cross-team collaboration to execute complex projects.
· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.
· Hire and develop top talent, provide technical and career development guidance to scientists and engineers in the organization.
· Analyze historical data to identify trends and support optimal decision making.
· Apply statistical and machine learning knowledge to specific business problems and data.
· Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed.
· Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
· Build decision-making models and propose effective solutions for the business problems you define.
· Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.
Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.
Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
Team video https://youtu.be/zD_6Lzw8raE
· PhD or Master’s Degree in Statistics, Applied Mathematics, Physics, Science, Engineering, Economics, or other quantitative fields.
· 3+ years of direct people management; managing scientists.
· 8+ years of experience as a data scientist, economist, applied scientist, research scientist or equivalent data analytics role
· Expertise in as many of the following: hypothesis testing, estimation, experimental design, hypothesis and A/B testing, causal inferencing, multi-variate testing & design, descriptive analytics, and regression analysis.
· Experience with data scripting languages (e.g. SQL, Python, R) or statistical/mathematical software (e.g. R, SAS, or Matlab).
· Familiarity with probability, probability distributions, statistics and causal inference.
· Good understanding of apply machine learning to solve real-world problems.
· Broad knowledge of ML methods, statistical analysis, and problem-solving skills.
· Expert level knowledge in statistics; sophisticated user of statistical tools.
· Experience processing, filtering, and presenting large quantities (hundreds of millions/billions of rows) of data
· Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization.
· Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
· Excellent verbal and written communication skills with the ability to advocate technical solutions for science, engineering, and business audiences.
· Ability to develop experimental and analytical plans for data modeling, use effective baselines, and accurately determine cause-and-effect relations.
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