Data Science Analytics
About the role
- Domain – Retail and/or CPG domain Knowledge
- Retail Execution – Demand Forecasting/Inventory Management/Supply Chain, CRM, Sales, and Operation, Procurement Analytics Knowledge - Clustering, Segmentation, Campaign Analytics, Loyalty Management, Forecasting
- Data Understanding of Sales, Inventory, Store, Product and Promotion Data
- Ability to translate business requirement to analytical model
- Communicate directly with business users
- Technical Skills –SQL, Advanced Excel, R/SASNice to have Skill/Competency: Hadoop, Spark, Python
- Understand business requirement from client and translate it to appropriate Analytics use cases
- Work in a team environment to deliver analytics solution to client
- Define new business metrics as required and build measurement systems as needed
- Enjoy designing, develop and deploy state of the art, data-driven predictive models
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
