Role Description & Responsibilities
Data Science
About Intel
Intel 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 and Analytics PhD Intern at Intel, 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 Intel'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 Intel
Verified EmployerIntel 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.