Senior Data Scientist, Retail Analytics

Key Skills

Java Programming, Leadership, Python Programming, SQL, AWS, Big Data Technology, Data science techniques, Data Visualization, MATLAB Programming, R Programming, Statistics, Amazon RedShift, Amazon Simple Storage Service (S3), SAS, Mathematics

Job Description

Do you want to shape the future of how customers search on Amazon? We are looking for a Senior Data Scientist (m/f/d) for our Retail Analytics team, who is passionate about big data and statistical modelling/machine learning (ML).

In this role you will analyze customer search data and work closely with product managers and science/engineering teams to make business recommendations and drive enhancements of our search customer experience. Beyond this you will lead strategic deep dives and build algorithmic solutions that allow us to delight Amazon customers at every turn.

The ideal candidate has a strong track record and end-to-end ownership of the full stack of data analysis (data engineering, ETL, data modelling/mining, and statistical/ML analysis), with a high fluency in Spark, SQL and Python, R or similar scripting and modelling software and expertise in the design, creation, management, and business use of large datasets. You should also thrive on independence and be relentless in finding automated solutions and eliminating manual processes, having hands-on experience with handling multiple improvement initiatives simultaneously.

You should excel in applying business and communication skills to be able to work with business owners to develop and define key business questions, and use appropriate algorithms, statistical or econometric techniques to derive insights and recommendations to leadership.

This position can be located in Munich or Berlin.

Key job responsibilities

• Analyze and understand Amazon’s search environment to identify meaningful improvement opportunities
• Challenge the status-quo by proposing and designing feature changes in centrally deployed models which reflect the German customer’s search behavior more accurately
• Work with central science, engineering and product teams to implement search CX improvements
• Work backwards from customer anecdotes to formalize assumptions about how particular features or models 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 are needed
• Dive deep into relevant insights to derive the overall strategy, targets and specific actions to increase the engagement of our customers. Build decision-making models and propose effective solutions for the problems or opportunities you define
• Use statistical and ML techniques to generate insights from big data and derive actions, as well as communicating results in front of senior leadership
• Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication.

A day in the life

Amazon search systems involve efficient information retrieval (IR) search algorithms, query reformulations, NLP, deep-learning algorithms to enable a superior shopper experience and render highly relevant products for a shopper’s mission. You will support data driven decision making that helps teams to experiment and innovate quickly on behalf of German customers.

You will become familiar with a diverse set of models, features and techniques in a high-volume, low-latency environment. Our search solutions play a key role in the business and operate under highly optimized settings, scanning billions of documents within milliseconds budget and ensuring high relevance. Quality and reliability are critically important in everything we do.

You’ll raise the bar on data science driven insights, researching and identifying new opportunities to improve matching algorithms and search result quality by investing in novel search features, key initiatives, and technical improvements to optimize for long-term business impact and customer satisfaction.

About the team

Retail Analytics’ key mandate is to improve our understanding of who Amazon's customers are, why and how they shop at Amazon, and what makes them come back or drives them away. Based on this understanding, we advocate the deployment of mechanisms to improve CX, engagement and retention.

The team provides analytical thought leadership to ask the right strategic questions and guide complex strategic decisions. Furthermore, we raise the overall level of analytical skills in the organization by advising and training other analytical roles.

BASIC QUALIFICATIONS

• MS in a quantitative field such as Computer Science, Mathematics, Statistics or related field
• Multiple years of relevant work experience in BI, Analytics, Data Science or comparable fields
• Proven experience with data querying languages (e.g. SQL), scripting languages (e.g. Python, R), or statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
• Relevant working experience in transformation, optimization and analysis of massive structured and unstructured data sets
• Track record of developing and applying statistical/ML models to business problems
• Experience in working on and delivering end-to-end projects independently

PREFERRED QUALIFICATIONS

• PhD in a quantitative field such as Computer Science, Mathematics, Statistics or related field
• Excellent written and verbal communication skills including data visualization, especially in regards to quantitative topics discussed with non-technical colleagues
• Experience articulating business questions and using quantitative techniques to arrive at a solution using available data
• Ability to influence multiple stakeholders to align roadmaps, priorities and drive change
• Strong organizational and multitasking skills with ability to balance competing priorities
• Familiarity with DDL and AWS services such as S3 and Redshift
• Experience with search engine optimization topics such as information retrieval and query classification
• Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
• Experience collaborating with software development teams, data scientists, business intelligence or other technical roles
• Ability to effectively advocate technical solutions to research scientists, engineering teams, and business audiences
• Ability to work in a fast-paced business environment.
• Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data
• Experience designing experiments, and ability to infer causal relationships