Sr Data Scientist, RBS Tech

Description

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of the customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also builds the science platforms that measure the causal business impact of these fixes at scale.

As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art causal-inference and classical ML solutions to solve problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to causal impact measurement of CX defects and interventions, quasi-experimental design (Difference-in-Differences, Double Machine Learning), treatment/control construction, propensity and matching methods, econometric and structural models, uncertainty quantification, and supervised and unsupervised learning applied to entitlement sizing, defect prioritization and impact validation across the Quasi Experimentation Platform (QEP).

Key job responsibilities
As a Senior Data Scientist, you will be responsible to design and deploy scalable causal-inference and classical ML solutions that measure the business impact of fixes affecting millions of customers and solve key customer experience issues. You will develop novel econometric, quasi-experimental and statistical techniques — Difference-in-Differences, Double Machine Learning (DR-DML, PLR-DML), synthetic control, matching and propensity methods, causal graphical models, pattern recognition, and anomaly detection. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML and causal models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide the team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Senior Data Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

Basic Qualifications

- 4+ years of data scientist experience
- Experience with statistical models e.g. multinomial logistic regression
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
- Experience in problem solving and delivering results
- Experience in oral and written communication
- Masters or PhD in Economics, Statistics, Electrical Engineering, Computer Science, Computer Engineering, Mathematics, or a related field with specialization in causal inference, econometrics, statistical machine learning, or related fields.
- Expertise in causal inference and econometric methods (e.g., Difference-in-Differences, Double Machine Learning, instrumental variables, matching/propensity methods, synthetic control) with a good working knowledge of classical machine learning.

Preferred Qualifications

- Experience as a leader and mentor on a data science team
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership
- Experience in software development
- Knowledge of computer science fundamentals in data structures, algorithm design, and problem solving
- Proven track record of managing science teams, hiring and developing science talent.
- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field of causal inference or impact measurement.
- Solid understanding of classical machine learning, causal inference and econometric methods, statistical modeling, and computational complexity.

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