Role Description & Responsibilities
Data Science
About P&G
P&G is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Data Science role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
As a Data Science New Grad - Master’s degree at P&G, you will join an innovative team focused on building scalable, data-driven solutions. Apply statistical analysis, machine learning algorithms, and predictive modeling to solve mission-critical challenges and extract actionable value from massive data assets.
This opening provides direct commercial exposure, structured technical mentorship, and the opportunity to contribute to production workflows across P&G's core business domains.
Key Responsibilities & Day-to-Day Scope
- ✓ Perform exploratory data analysis (EDA), statistical hypothesis testing, and rigorous feature engineering on multi-dimensional datasets.
- ✓ Design, train, validate, and tune predictive machine learning models using Python (scikit-learn, XGBoost, LightGBM).
- ✓ Collaborate with software and data engineers to integrate model inferences into production services and APIs.
- ✓ Design and evaluate A/B experiments to measure the commercial impact of product features and algorithmic enhancements.
- ✓ Synthesize complex statistical findings into clear executive summaries and technical documentation.
Official Qualifications & Technical Requirements
- • Enrolled in or graduated from a Bachelor's, Master's, or PhD program in Data Science, Computer Science, Statistics, Mathematics, Physics, or related quantitative field.
- • Strong programming proficiency in Python and standard scientific computing libraries (pandas, numpy, scipy, scikit-learn).
- • Solid theoretical and practical foundation in probability, statistics, regression, classification, and validation techniques.
- • Working knowledge of SQL for pulling and structuring training datasets from petabyte-scale repositories.
- • Curiosity to explore unstructured data and translate open-ended business problems into quantitative solutions.
Preferred Skills & Technology Stack
- + Familiarity with distributed compute frameworks (Apache Spark, Databricks).
- + Experience with experiment tracking tools (MLflow, Weights & Biases) and containerization (Docker).
- + Personal GitHub repository or competition profile (Kaggle) demonstrating practical data science projects.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at P&G
Verified EmployerP&G champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.