Data Scientist, Ads Marketing Decision Science
Amazon - new york city, NY
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Description We are looking for a motivated and experienced Data Scientist with experience in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture with deep understanding in advertising businesses, to be part of a team of talented scientists and engineers to innovate, iterate, and solve real world problem with cutting-edge AWS technologies. In this role, you will take a leading role in defining the problem, innovating the ML/AI solutions, and information the tech roadmap. You will join a cross-functional, fun-loving team, working closely with scientists and engineers on a daily basis. You will innovate on behalf of our customers by prototyping, delivering functional proofs of concept (POCs), and partnering with our engineers to productize and scale successful POCs. If you are passionate about creating the future, come join us as we have fun, and make history. Key job responsibilities Define and execute a research & development roadmap that drives data-informed decision making for marketers and advertisers Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale Partner with product managers and stakeholders to define forward-looking product visions and prospective business use cases Drive and lead of culture of data-driven innovations within and outside across Amazon Ads Marketing orgs About the team Marketing Decision Science provides science products to enable Amazon Ads Marketing to deliver relevant and compelling guidance across marketing channels to prospective and active advertisers for success on Amazon. We own the product, technology and deployment roadmap for AI- and analytics-powered products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right team to help them make the data-informed advertising decisions. Our science-based products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases. Basic Qualifications 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience 2+ years of data scientist experience 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience Bachelor's degree Experience applying theoretical models in an applied environment Preferred Qualifications Experience in Python, Perl, or another scripting language Experience in a ML or data scientist role with a large technology company 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 Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.
Created: 2024-11-05