Data Scientist - Personalization

Data/Measurement Scientist -- Personalization
Global Consumer Knowledge

Nike does more than outfit the world's best athletes. We are a place to explore potential, obliterate boundaries, and push out the edges of what can be.

Nike's Global Consumer Knowledge Center of Excellence is a growing team responsible for building and deepening a holistic view of Nike's consumers through data and analytics, and applying those insights to inform the development of incredible digital services and experiences for our consumers.

We are looking for a scientist with tech industry experience to help build our core data products and modeling/experimentation processes. We want someone who loves modeling and writing code-who likes to get their hands dirty. This is a young and growing team, so the ideal candidate is ready for a leadership role, defining and socializing best practices for the team.

We are looking for a seasoned hand. Someone who is comfortable with state of the art modeling or machine learning algorithms, but can think in terms of the compute process and coupling between systems.

We're looking for someone who thrives in a dynamic setting. Communication and leadership skills are key. You'll get to work with scientists with a breadth of experiences across industry and academia, including in machine learning, statistics, epidemiology, and physics. You'll be expected to go deep and learn with them, as a peer.


Qualifications
Key responsibilities:

• Develop models that help us understand and describe our customers, e.g. learning how to extract deep interests and tendencies from event streams.
• Manage an experimentation portfolio that validates and feed back into our core customer understandings.
• Build processes to support fast, iterative experimentation, both for model creation and for customer-facing products.
• Define best practices for scientific code development and deployment.

Qualifications

Education:
• B.S. in CS, statistics, applied math, physics or other quantitative discipline
• Advanced degree (Masters or PhD) preferred

Experience
• 2 + years post grad expereincein a role developing predictive or explanatory models and/or experimentation processes
• Experience working with data and analytics
• Experience with moderate to large-scale data sets (>100GB) preferred
• Experience as a lead preferred
• Experience with neural nets and deep architectures preferred

Required Skills
• Core mathematical ability to understand, utilize and innovate on state-of-the art machine learning algorithms and/or statistical modeling
• Expertise in at least one production-quality programming languages (e.g. java, python, C++)
• Exceptional communication skills
• Expertise with Hadoop ecosystem (mapreduce, hive, pig, spark) is a plus
• Expertise with ETL processes a plus

Key responsibilities:

• Develop models that help us understand and describe our customers, e.g. learning how to extract deep interests and tendencies from event streams.
• Manage an experimentation portfolio that validates and feed back into our core customer understandings.
• Build processes to support fast, iterative experimentation, both for model creation and for customer-facing products.
• Define best practices for scientific code development and deployment.

Qualifications

Education:
• B.S. in CS, statistics, applied math, physics or other quantitative discipline
• Advanced degree (Masters or PhD) preferred

Experience
• 2 + years post grad expereincein a role developing predictive or explanatory models and/or experimentation processes
• Experience working with data and analytics
• Experience with moderate to large-scale data sets (>100GB) preferred
• Experience as a lead preferred
• Experience with neural nets and deep architectures preferred

Required Skills
• Core mathematical ability to understand, utilize and innovate on state-of-the art machine learning algorithms and/or statistical modeling
• Expertise in at least one production-quality programming languages (e.g. java, python, C++)
• Exceptional communication skills
• Expertise with Hadoop ecosystem (mapreduce, hive, pig, spark) is a plus
• Expertise with ETL processes a plus


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