Data Scientist

Nike Global Marketplace Operation

Consumer Fulfillment Insights

Data Scientist (ETW)

The key function of post purchase fulfillment insights is to enable our ability to monitor, track and analyze the overall strength and health of Nike’s post purchase experiences through qualitative and quantitative approaches with the goal of helping Nike business partners diagnose and prioritize friction to improve Post-Purchase experiences across the marketplace, particularly in consumer touchpoints owned by Supply Chain.

We are looking for a Data Scientist who has a deep passion for applying advanced analytic approaches, an eagerness to dig into the data, and a vision for turning data streams into a cohesive view of consumer experience and expectation. The ideal candidate excels in a cross-functional teams and thrives in a fast-pace organization where there is an opportunity to meaningfully evolve the way Nike connects with its consumers through Omni channel. In this role, you’ll work as an analytics expert using consumer engagement data to identify opportunities to optimize consumers post purchase experience and business strategy in supply chain through understanding consumer behaviors, experience and expectation.

Key responsibilities:

• Supporting cross-functional teams in Supply Chain to identify and measure frictions and opportunities for consumer post purchase experience optimization.
• Providing a comprehensive view of relevant consumer experience and expectation, incorporating a wide variety of signals, data types, and sources.
• Developing comprehensive analytics and data modeling strategies, implementing effective quantitative methodologies, and overseeing the entire lifecycle of advanced analytics and statistical modeling processes.

Qualifications:

• Bachelor’s degree in computer science, data science, Statistics, Applied math or a related quantitative field. Advanced degree preferred.
• 3+ years’ consumer analytics and statistical modeling experience in e-commerce/consulting/internet industry.
• Strong hands-on experience in researching, developing, and building predictive models (linear regression, logistic regression and time series, etc.), consumer segmentation and causal inference analysis through Statistical techniques.
• Proficient in SQL, Python, R or similar programming languages in analyzing large structured and unstructured datasets and building statistical models.
• Ability to understand and implement complex algorithms efficiently and correctly, especially from academic articles and published research results.
• Strong PPT/Keynote skill is preferred.