Lead Data Scientist - Testing/Measurement (MN)

4 days left

Employer
Target
Location
Minneapolis, Minnesota, United States
Posted
Oct 07, 2018
Closes
Oct 26, 2018
Category
Business, Other
Employment Status
Full Time
Description: Lead Data Scientist

Join Target's Testing & Measurement team where your goal will be to accelerate omni-channel experimentation to foster a culture of continuous innovation at Target.We believe that continuous innovation is critical to delivering and exceeding our brand promise of “Expect More Pay Less” to Target's guests irrespective of their preferred channel of engagement and commerce.You will enable continuous innovation and drive business performance via testing and measurement (experimentation).

In this role you will have three key priorities - develop cutting edge scientific methods for testing and measurement of enterprise growth initiatives, drive adoption of scientific methods by partnering with business owners across the enterprise, drive implementation of new methods on our high-throughput testing platform by partnering with internal data and technology teams.

Specifically, you will lead the following efforts:

·Develop advanced statistical and data science methods to drive lean and efficient testing practice at enterprise scale.

·Partner with product and business teams to translate hard and abstract problems into well defined, quantifiable and measureable initiatives.

·Define key business metrics for running large scale experiments that deliver impactful results to the business.

·Design experiments to evaluate the optimization of guest experience at Target agnostic of channel (online or in physical stores).

·Partner with internal data and technology teams to implement scalable solutions for testing and measurement.

Requirements:

·Masters or PhD in statistics, applied mathematics, computer science, econometrics or an equivalent domain.

·3+ years of experience in large scale A/B and multivariate testing, or other relevant work in design of experiments.

·Expertise in either statistical modeling or machine learning (deep learning).

·Proficiency in data analytics languages such as SQL, R, Python.

·Proficiency in big data technologies such as Hadoop, Hive, Spark.

·Strong communication skills to influence cross functional teams.



Qualifications:

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