Data Scientist

**The Role**:
**Data Scientist (CRM)**:
Job Group Level: 5

General Position Definition
- This role will responsible for delivering analytics and insights for the Global CRM & Loyalty teams in Shell’s Retail business
- Individual will be a technical expert on a range of technologies and tools

Purpose
- Leverage analytics, data and deep customer insights to identify and execute innovative programs that continually push CRM initiatives to the next level
- Build and maintain a test and learn roadmap (A/B Testing) and work to continually iterate and optimize CRM efforts in order to increase the effectiveness of all channels
- Support development of the contact strategy with ad-hoc analysis and projects.
- Lead the design and development of CRM reporting dashboards; establish templates and processes for regularly sharing out CRM progress and results with key stakeholders
- Develop and evolve advanced segmentation strategies for targeted campaigns driving incremental revenues

**Skills**:
Industry / Functional Expertise
- Provide deep business expertise in:

- ** CRM, Loyalty Analytics**:Strong Campaign Analytics experience in both online & offline. Must have worked with Campaign, Loyalty and CRM data. Understands campaign effectiveness study and experienced in planning/targeting for CRM activities based on learning from previous activities
- ** Marketing / Customer Analytics**: Campaign design and effectiveness testing, churn prediction, cross-sell / up-sell, Market Basket Analysis, Customer segmentation, propensity analysis, customer lifetime value
- Preferably worked in the past in Retail companies
- Paid Media Experience is also preferable and will be an added advantage
- Proficiency Level**:Mastery**

Stakeholder Engagement Skills
- Working collaboratively across multiple sets of stakeholders - business SMEs, IT, Data teams, Analytics resources, etc. to deliver on project deliverables and tasks
- Identify actionable insights that directly address marketing challenges / opportunities
- Articulate business insights and recommendations (based on model output) to respective stakeholders
- Understanding business KPI's, frameworks and drivers for performance
- Proficiency Level**: Mastery**

Technology Skills
- 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
- Adobe Analytics (good to have)
- ** Statistics / Mathematics**: Data Quality Analysis, Data identification, Hypothesis testing, Univariate / Multivariate Analysis, Cluster Analysis, Classification/PCA, Factor Analysis, Linear Modeling, Affinity & Association, Time Series
- 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**

Special Challenges
- ** Must have Skills**:

- Knowledge of Direct marketing and CRM principles (segmentation, lifecycle management, etc.).
- Familiarity with the marketing funnel and customer journey frameworks to guide strategy
- 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 or marketing 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

Experience
- 5-9 years of relevant experience in Marketing / Customer Analytics
- Strong team management skills
- Preferred experience in Retail or E-commerce managing CRM and Loyalty Data
- 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, OR, etc.
**Disclaimer