FedEx - Data Scientist - Customer Analytics

Responsibilities-

• Deep-dive into Customer Behavior: Analyze customer interactions across various touchpoints to identify patterns and trends.
• Segmentation: Create customer segments based on behavioral, demographic, and other relevant factors to tailor marketing strategies.
• Predictive Modelling: Develop predictive models to preempt customer churn, identify high-value customers, develop growth & loyalty models.
• Maximize Customer Lifetime Value (CLTV): Develop CLTV to assess long-term customer worth and prioritize retention efforts.
• Data-Driven Decision Making: Provide actionable insights and recommendations to improve customer acquisition, retention, upselling, and cross-selling.
• Cross-Functional Collaboration: Work closely with various cross functional teams (internal analytics team, marketing, sales, and other commercial teams) to build consensus and align data-driven insights with business goals.
• Strong domain knowledge/ Business acumen: Previous experience in marketing/Customer analytics roles aligns well with the requirements of the position. Proven track record of leveraging data to drive customer acquisition, growth and retention efforts.
• Develop and deploy Data science/machine learning models on Azure cloud platform
• Implement data pipelines using Azure Data Factory to automate data ingestion and transformation.
• Collaborate with other data scientists & MLOps engineers to operationalize advanced models on Azure.

Qualifications & Specifications-

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Marketing, or Management. Advanced degree preferred.
• Proven Data Science Expertise: Minimum of 5 years of experience in data science, with a strong focus on marketing and customer analytics.
• Cloud Proficiency: Demonstrated experience building and deploying models on cloud platforms, preferably Azure.
• Cloud Technologies: Strong familiarity with Microsoft Azure tools and services, including Azure Data Factory and Databricks.
• Advanced Modeling and Optimization: Proven ability to apply advanced statistical and machine learning techniques to solve complex customer lifecycle problems.
• Data Visualization and Storytelling: Exceptional skills in data visualization to effectively communicate insights to both technical and non-technical audiences.
• Effective Communication: Strong written and verbal communication skills, including the ability to present complex technical concepts to senior leadership and executive teams.
• Insight Generation: A keen eye for identifying trends, patterns, and opportunities within data.
• Problem-Solving: A strong problem-solving mindset and the ability to think critically to address challenges.