General Position Definition
- This role will drive analytics and data science for one or more of Shell’s Retail business teams
- The ideal candidate is passionate about delivering commercial value and insights through Retail Analytics and Data Science
- We help the business team’s answer some of the following questions:
- What is the right assortment and mix of products to drive category growth?
- How responsive are our products to price changes?
- Which of our competitors impact our sales and pricing?
- How can we improve the customer experience at our outlets?
- Which customers are most likely to churn?
- Candidate should be able to ask the right questions, ability to move from data to insight to action, break down strategic & operational questions from different Retail teams and structurally answer them with data driven insights
- Incumbent is responsible for working on a range of technologies and tools collaborating directly with the Retail stakeholders & other partners
Education Requirement/Field of Study :
- Minimum 5-7 years of relevant experience in Retail Analytics
- Preferred experience in Retail, CPG or E-commerce
- Good interpersonal communication skills and influencing skills
- Eagerness to learn and ability to work with limited supervision
- Advanced university degree in Mathematics, Statistics, Engineering, Economics, etc.
Requirements : Skills
Industry / Functional Expertise
- Provide deep business expertise in Retail/CPG businesses:
- Category Analytics:Range reviews, Assortment planning, identifying trends and patterns in category performance, new product launches
- Pricing Analytics:UnderstandingPrice elasticity, evaluating impact of competitor pricing, impact of pricing changes on margins
- Marketing and Promotion Analytics: Campaign design and promo effectiveness testing, churn prediction, cross-sell / up-sell, Market Basket Analysis, Customer segmentation, propensity analysis, customer lifetime value, market mix modeling
- Store Analytics:Leverage data and visualization to create actionable insights for on ground store teams, territory and district managers
- Should have strong story telling skills: Ability to explain complex data and models to business teams
- Proficiency Level:Mastery
Stakeholder Engagement Skills
- Working collaboratively across multiple sets of stakeholders – business SMEs, IT, Data teams, Analytics resources to deliver on project deliverables and tasks
- Identify actionable insights that directly address Retail Team’s challenges / opportunities
- Articulate business insights and recommendations to respective stakeholders
- Understanding business KPI's, frameworks and drivers for performance
- Proficiency Level:Mastery
- Strong experience in specialized analytics tools and technologies (including, but not limited to)
- Azure Databricks, Alteryx
- Power BI, Spotfire or other visualization tools
- Python, R
- Statistics / Machine Learning: Data Quality Analysis, Exploratory data analysis, Hypothesis testing, Univariate / Multivariate Analysis, Classification/PCA, Factor Analysis, Linear Modeling, Affinity & Association, Time Series, Decision Trees, sentiment analysis, Clustering
- Identify the right approach(es) for given scenario and articulate why the approach fits
- Assess data availability and modeling feasibility
- Proficiency Level:Skill-to-Mastery
- Easily analyzes, draws and synthesizes important insights from complex data.
- Extremely curious and self-driven to understand business performance through data.
- Ability to translate a business question into a well-defined analytical plan that includes data requirements for technical resources to extract the necessary data.
- Rapid onboarding on projects, understanding analytics goal and working with ill-defined datasets
- Communicating technical jargon in plain English to colleagues within Data Science team and outside
- Virtual working with network of colleagues located throughout the globe