EcoVadis offers an exciting, truly international job opportunity in an innovative and dynamic environment. Awarded as the fastest-growing startup in the sustainable supply chain space, EcoVadis is driven by a young team of over 40 nationalities making a real impact on the environmental and social practices of companies around the world. We aim at improving environmental and social practices of companies by leveraging the influence of global supply chain. More than 150 global multinational companies have selected the EcoVadis solution, including Verizon, GSK, Coca Cola Enterprises, Nestle, Johnson & Johnson, Renault, ING Bank, and Subway!

Overview

We are looking for a highly motivated Data Scientist Intern to join our growing EV Labs Team, responsible for using machine learning to drive innovation across the organization. As this is a newly created position, you must thrive in a start-up environment, be proactive, detail oriented and eager to learn and keep-up with an evolving product landscape.

What will you be doing?

You will be assigned to one or several products or projects, and be in charge of identifying and quantifying opportunities for optimizations and solve issues as they arise.

Among the tasks foreseen:

• Leverage data to solve business problems.

• Uncover trends, patterns, correlations and other relationships in data and deliver actionable business insights from this information by creating algorithms and advanced analytic models using statistical, data mining and / or machine learning tools and techniques.

• Build and maintain data-driven optimization models, experiments, forecasting algorithms, and machine learning models.

• Benchmark your model against the current ones.

• Leverage tools like R, Python, Hadoop & SQL to drive efficient analytics.

Who we are looking for people:

• Eager to learn attitude for logical problem-solving skills.

• Enthusiastic, can-do attitude who can work independently as well as contribute to the team.

• Strong communication skills both within and outside teams.

• Demonstrable software engineering experience from previous internship, work experience, coding competitions, or publications preferably in Python.

• Any involvement in a project to implement a machine learning model from scratch would be a distinct advantage.

What you will need to be successful:

• Comprehensive understanding of statistics and modeling techniques and Machine Learning.

• Python and/or R for statistical analysis, visualization and modeling (pandas, scikit-learn, nltk, etc...).

• SQL.

• Proficiency in Python.

• Demonstrate past projects (Kaggle or else) where you used Deep Learning.

• Autonomous, detail-oriented.

• Enjoy working as a team-player and learning from others.

• Fluent in English (French is not mandatory).

Bonus:

• You have worked with cloud tools (Azure, AWS, GCP) and you have deployed the model in a production environment.

• First professional experience as a Data Scientist.

• Experience in Deep Learning from TensorFlow or Keras.

You can expect us to review your application within 1 week of its submission. If your application fits our requirements, we will first invite you to an interview call followed by a case study and 2 onsite interviews.

Give your career a boost! Join an outstanding team of talented individual working on a project that makes sense...

What EcoVadis offers:

•    The opportunity to work in an autonomous way and in a dynamic environment where your new ideas will be valued.

•     Be part of a young, exciting and diverse global team.

EcoVadis does not discriminate based on race, religion, color, national origin, gender, sexual

orientation, age, disability, veteran status or any other protected category.

Beginning: ASAP

Employment Type: Six months Internship 
 

Location: Paris

For more information about EcoVadis please visit our site: www.ecovadis.com

EcoVadis in 2 minutes: http://intro.ecovadis.com/

EcoVadis does not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, disability, veteran status or any other protected category.