Data Scientist/Architect
Work options: Remote
Remote, US
Title: Data Scientist/Architect
Location: Boston, MA
Duration: 12 month contract
NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At Nike, it’s about each person bringing skills and passion to a challenging and constantly evolving game.
What You Will Do
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- Design, develop, and program methods, processes, and systems to consolidate and analyze structured/unstructured, diverse “big data” sources.
- Develop software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources.
- Interact with product and service teams to identify questions and issues for data analysis and experiments.
- Identify meaningful insights from large data and metadata sources; interpret and communicate findings to product, service, and business managers.
- Lead the accomplishment of key goals across consumer and commercial analytics functions.
- Work with stakeholders to understand requirements, develop sustainable data solutions, and provide insights and recommendations.
- Document and communicate systems and analytics changes to the business, translating complex functionality into business-relevant language.
- Validate key performance indicators and build queries to measure business performance quantitatively.
- Communicate with cross-functional teams to understand the business cause of data anomalies and outliers.
- Develop data governance standards from data ingestion to product dictionaries and documentation.
- Use data to predict trends, perform statistical analysis, and extract information from datasets.
- Monitor data quality and remove corrupt data.
- Evaluate and utilize new technologies, tools, and frameworks centered around high-volume data processing.
- Improve existing processes through automation and efficient workflows.
- Build and deliver scalable data and analytics solutions.
- Work independently and take initiative to identify, explore, and solve problems.
- Design and build innovative data and analytics solutions to support key decisions.
- Support standard methodologies in reporting and analysis, including data integrity, testing, modeling, validation, and documentation.
What You Will Work On
- Consolidating and analyzing structured/unstructured, diverse “big data” sources.
- Developing software programs, algorithms, and automated processes for data cleansing, integration, and evaluation.
- Collaborating with product and service teams to identify questions and issues for data analysis and experiments.
- Communicating findings and insights to product, service, and business managers.
- Leading and supporting key goals across consumer and commercial analytics functions.
- Implementing data governance standards and documentation.
- Utilizing data to predict trends, perform statistical analysis, and extract valuable information.
- Monitoring and ensuring data quality.
- Evaluating and incorporating new technologies for high-volume data processing.
- Improving processes through automation and efficiency.
- Delivering scalable data and analytics solutions.
- Identifying and solving problems independently.
What You Will Bring
- Bachelor’s degree and a minimum of 8 or 9 years of directly relevant work experience.
- Proficiency in Python, SQL queries and data visualization.
- Strong analytical and problem-solving skills.
- Ability to communicate complex concepts to non-technical stakeholders.
- Experience with data governance and documentation.
- Knowledge of statistical analysis and data mining techniques.
- Familiarity with data quality control and testing methodologies.
- Capacity to work independently and take initiative.
- Aptitude for designing and implementing innovative data solutions.
- Understanding of reporting and analysis best practices.