Data Scientist

About the Role

The hired candidate will partner with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units. The incumbent will be responsible for delivering advanced analytics projects that drive business results including interpreting business, selecting the appropriate methodology, data cleaning, exploratory data analysis, model building, and creation of polished deliverables.

Responsibilities

• Analyze large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business

• Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data

• Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions

• Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data

• Structure hypothesis, build thoughtful analyses, develop underlying data models, and bring clarity to previously undefined problems

• Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines, and data workflows to automate dashboards and analyses

• Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats

Qualifications and experience

• Bachelor’s degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.)

• Master’s degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.)

• 3–4 years of relevant working experience in a data science/advanced analytics role

• Knowledge of Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics)

• Knowledge and ability to conduct statistical modelling using Analytical tools (R, Python, KNIME, etc.) and use big data technologies

• Knowledge of business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)

• Knowledge of Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational database management systems and Data Engineering tools

• Knowledge and ability to use Big data technologies (Hadoop, Spark, Kafka, Presto etc) and Cloud computing services in Azure/AWS/GCP for data engineering, ML Ops

• Ability to delivery, strong disposition towards business and strong interpersonal communication

• Individual must be organized, dependable, able to multi-task and manage priorities, display initiative, and must have the ability to work independently in a demanding, fast-paced environment.

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