Works independently with Business stakeholder and deliver Data Science projects, which requires the job holder to apply a combination of applied and theoretical knowledge within a well-defined scope. Has knowledge about a specific discipline and may coach less experienced colleagues.
Works with data and technology experts to help execute analytics projects and deploy solutions.
Develops optimal analytical models that address the business requirement, with acceptable levels of accuracy.
Writes efficient code that incorporates best practices in that technology.
Supports the design and delivery of analytics projects, within or cutting across different business units in Shell.
Hands-on experience working with large and often complex data, also should have strong analytical skills and story-telling skills to evaluate and interpret trends and summarize them in a great story
The incumbent should work closely with the stakeholders day-to-day and will be the technical specialist for the business across the solutioning journey: ask the right questions, ability to move from data to insight to action. Break down strategic & operational questions from different teams, structurally answer them with data driven insights / advanced analytics solutions, responsible for implementation and tracking of results.
Required Skills And Experience
5+ years of relevant experience in Analytics / Data Science
Broad experience and knowledge in Machine Learning
Preferred experience in Retail, CPG or E-commerce
Preferred experience in Pricing
Expert in Databricks
A practical common-sense approach to problem solving and attention to detail
A passion for and expertise in practicing data science to solve real-world customer problems
Excellent oral and written communication skills
Strong interpersonal skills, influencing skills and enthusiasm for teamwork, as well as the ability to work independently
High standards of code quality, making use of version control tools
Skills - Industry Expertise
Provide deep business expertise in Retail/CPG businesses in atleast one of the below areas:
Category Analytics: Range reviews, Assortment planning, identifying trends and patterns in category performance, new product launches
Pricing Analytics: Understanding Price elasticity, evaluating impact of competitor pricing, impact of pricing changes on margins