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.