Senior Data Scientist - Stakeholder Solutions, CMO

Key Skills

Java Programming, Analytical and Problem solving, Design, Python Programming, SQL, Big Data Technology, Apache Hadoop, Scala Programming, Data science techniques, A/B Testing, Deep Learning, Data Modeling, Apache Hive, Apache Pig, Elasticsearch, R Programming, Amazon EMR, PySpark, SPARK Programming, Large scale data processing

Job Description

The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazons marketing teams’ decisions and thus how Amazon customers see, use, and value their experience.

CMO (Campaign measurement and Optimization) team within CBA org's mission is to make Amazon’s marketing the most measurably effective in the world. Our long-term objective is to measure the incremental impact of all Amazon’s marketing investments on consumer perceptions, actions, and sales. This requires measuring Amazon’s marketing comparably and consistently across channels, business teams and countries using a comprehensive approach that integrates all Paid, Owned and Earned marketing activity. As the experts on marketing performance, we will lead the Amazon worldwide marketing community by providing critical global insights that can power marketing best practices and tenets globally.

Are you passionate about Deep Learning, Causal Inference, and Big Data Systems? Interested in building new state-of-the-art measurement products at petabyte scale? Be part of a team of industry leading experts that operates one of the largest big data and machine learning stacks at Amazon. Amazon is leveraging its highly unique data and applying the latest machine learning and big data technologies to change the way marketers optimize their advertising spend. Our campaign measurement and reporting systems apply these technologies on many billions of events in near real time.

You'll be one of the lead scientists tackling some of the hardest problems in advertising; measuring ads incrementality, providing estimated counterfactuals and predicting the success of advertising strategies for omni-channel campaign measurement. Working with a cross-functional team of product managers, program managers, economists and engineers you will develop state of the art causal learning, deep learning, and predictive techniques to help marketers understand the performance of their omni-channel campaigns and optimize their spends.

Some things you'll do in this role:

• Lead full life-cycle Data Science solutions from beginning to end.
• Deliver with independence on challenging large-scale problems with complexity and ambiguity.
• Write code (Python, R, Scala, SQL, etc.) to obtain, manipulate, and analyze data.
• Build Machine Learning and statistical models to solve specific business problems.
• Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
• 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.

Impact and Career Growth: You will invent solutions that can make billion dollar impact for Amazon as an advertiser. Define a long-term science vision for our 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.

Key job responsibilities

• Dive deep into petabyte-scale data to drive insights, identify machine-learning modeling gaps and business opportunities

• Establish scalable, efficient, automated processes for large-scale data analysis

• Run regular A/B experiments, gather data, and perform statistical analysis

• Work with scientists, engineers and product partners to develop new machine learning approaches, and monetization strategies

• Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication

BASIC QUALIFICATIONS

• Experience in as many of the following areas: causal inferencing, multi-variate testing & design, A/B testing & design, descriptive analytics, and regression analysis.
• Good understanding of supervised and unsupervised learning models.

PREFERRED QUALIFICATIONS

• Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.
• Broad knowledge of ML methods, statistical analysis, and problem-solving skills.
• Expert level knowledge in statistics; sophisticated user of statistical tools.
• Experience in data applications using large scale distributed systems (e.g. EMR, Spark, Elasticsearch, Hadoop, Pig, PySpark, and Hive).
• 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.