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Cincinnati, OH

Internship

Onsite

$29/hr - $50/hr

Intern

Procter & Gamble integrates business, innovation, and technology through Data Science to create competitive advantage and deliver technology that helps win with consumers. The Data Scientist Intern will use big data and advanced analytics to support business leaders, develop research and analytical models, collaborate with engineering teams, and turn insights into measurable recommendations.

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Responsibilities

**Algorithm Design & Development:** Directly contribute to the design and development of scalable Data Science algorithms
**Collaboration:** Work closely with Data and Software Engineering teams to effectively productionize algorithms
**Data Analysis:** Apply thorough technical knowledge to large datasets, conducting exploratory data analysis, feature engineering, and model building
**Coaching & Mentorship:** Develop others as a technical coach, sharing your expertise and insights
**Expertise Development:** Become a known expert in one or multiple Data Science techniques and methodologies
As an intern, you will experience a subset of the above responsibilities

Qualification

Required

You must be available during the summer of 2027 for a 12 week internship from mid/late May through early August
Currently pursuing a Master's in Data Science, Statistics, Applied Mathematics, Physics, Computer Science, Business Analytics, Operation Research, Software Development, or a similar quantitative field

Preferred

Proven leadership in applying and scaling Analytic techniques to deliver measurable insights from data
Strong written and verbal communication skills to influence others to take action
Proven ability to balance multiple priorities
Comfort working with diverse business scenarios and possessing strong thinking/problem-solving skills to business processes with a "can-do" attitude
Strong passion and curiosity about the intersection of business and technology
Preferred Experience with Analytical Tools/Applications:
Unix/Linux
Big Data Ecosystem: Hadoop, Spark, MapReduce, SQL, Hiv
Scientific Computing: R, Python, C++, Java, Scala
High-Performance Parallel and Distributing Computing
Deep Learning frameworks: Keras, Tensorflow
Data Visualization
Data Management Systems
Business Intelligence tools: such as KNIME, Tableau