DATA SCIENCE INTERN - DIGITAL MARKETING

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

Do you want to work on iconic brands like Ariel, Pampers, Gillette, Head & Shoulders or Oral-B?Have responsibilities and a rich career with rapid prospects for development? Then, the Information Technology function at P&G is for you!You will join a dynamic company with a strong team spirit, that will heavily invest in you and your training.

By joining P&G, you will be given responsibilities as of Day 1. You will feel the ownership of your project from the beginning, and you will be given specific projects and responsibilities. You will evolve in adynamic and supporting work environmentwhere employees are at the core. Wevalue every individual and encourage initiatives, promoting agility and work/life balance.

YOUR CONTRIBUTION TO P&G’S SUCCESS:

• Develop and maintain scalable data pipelines that will ingest, transform, and distribute numerous data streams and batches in support of key CRM and Media Initiatives.
• Support and collaborate with Data Scientists developing sophisticated machine learning and statistical models.
• Automate pipelines to capture, integrate and clean data to support edge analytics solutions.

Qualifications

YOU ARE THE RIGHT FIT IF YOU HAVE/ARE:

• Studying a Master’s degree in a quantitative field (e.g. Engineering, Data Science, Statistics, Computer Science, Physical Sciences) or Quantitative Social Sciences.
• Proficiency in English and in French.
• Strong leadership and communication skills.
• Strong problem-solving skills paired with extensive experience programming (Python, Java, C++, etc.)
• Hands on experience with relational databases and the use of SQL to extract and manipulate data.
• Preferred Experience with Analytical Tools/Applications such as Unix/Linux; Big Data Ecosystem: Hadoop, Spark, MapReduce, SQL, Hiv; Scientific Computing: Python, C++; High-Performance Parallel and Distributing Computing; Deep Learning frameworks: Keras, Tensorflow; Experience with cloud services (especially GCP); Familiarity with machine learning workflow.