About the Role :

The Data Scientist II is a hands- on contributor responsible for delivering applied data science, advanced analytics, and machine learning solutions on Google Cloud Platform (GCP) for FedEx Office data applications.

This role focuses on executing data science solutions that drive business outcomes, working closely with data engineers, software engineers, and business stakeholders.

The role emphasizes practical, production- oriented data science rather than pure research.

The Data Scientist II may occasionally support data engineering activities to enable ML pipelines and ensure data readiness, while continuously building depth in modeling, analytics, and ML deployment.

Key Responsibilities :

- Understand business problems and translate them into data science and analytics use cases.

- Perform exploratory data analysis (EDA) and feature engineering on large, complex datasets.

- Build, train, and evaluate statistical and machine learning models (descriptive, diagnostic, predictive, and prescriptive).

- Apply appropriate modeling techniques to generate actionable insights and recommendations.

- Support model deployment and lifecycle management using Vertex AI, following established patterns.

- Collaborate with data engineers to ensure high- quality, ML- ready datasets.

- Write production- quality Python and SQL for data preparation, transformations, and feature pipelines.

- Participate in code reviews, testing, and medium- scale deployments.

- Follow and contribute to data science standards, reusable templates, and best practices.

- Work closely with cross- functional teams (IT, business, analytics, engineering) in an Agile environment.

- Assist with troubleshooting data or model issues in production under guidance.

- Continuously learn and apply emerging data science and ML techniques in a business- focused manner.

GCP Technology Stack :

- Programming: Python, SQL

- GCP Analytics: BigQuery, Cloud Storage

- AI/ML: Vertex AI (model training, deployment, monitoring - working knowledge)

- ML Libraries: scikit- learn, XGBoost, TensorFlow or PyTorch (as applicable)

- DevOps / MLOps: Git, CI/CD (foundational exposure)

- BI / Visualization: Power BI or Looker

Skills & Qualities :

- Strong foundation in data science, statistics, and machine learning concepts

- Experience delivering applied analytics or ML solutions in real- world environments

- Ability to work across data science and light data engineering tasks

- Engineering mindset with focus on code quality and reproducibility

- Strong analytical thinking and structured problem- solving skills

- Effective communication and collaboration with cross- functional teams

- Willingness to learn, adapt, and grow toward more complex data science ownership

Qualifications :

- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field

- Typically 3-5 years of relevant experience in data science, analytics, or ML roles

- Experience supporting end- to- end analytical or ML workflows, from data exploration to production deployment