Data Science - Internship
Tinyclues, France

Tinyclues’ AI-first marketing platform is built with the latest technology including ML frameworks like PySpark and TensorFlow, Kubeflow, Kubernetes, Docker or React. Our software stack is based on the Python ecosystem, natively designed for the cloud and deployed on AWS. We focus on using those technologies for what they do best.

The solution processes and analyzes hundreds of terabytes of data every day from our 100+ enterprise clients across 13 countries. It runs dozens of powerful and carefully designed Deep Learning algorithms to find the tiny clues in our clients’ first-party databases.

We believe empowering people is the most efficient way to build the best product together. The teams are organized in small amp; autonomous groups working on different business needs with agile methodologies.

Our innovative technology has led Tinyclues to be identified by leading IT analyst firm Gartner as a Vendor to Watch for digital marketing analytics and a Cool Vendor in multichannel marketing.

By joining Tinyclues, you will have an impact on one of top fast-growing start-ups with a unique AI-first vision, breakthrough predictive technology and proven global success.

• Deep Learning for Recommendation Systems: How to combine implicit amp; explicit feedback in a deep learning framework (factorizations, higher level interactions, feature transfer, .)
• Attention Pooling Networks: How to aggregate a customer's sequence of historical events into a single meaningful vector
• Cold start problem: How to predict a future event for a user or a product that was never seen in a historical dataset
• Meta Learning: How to tune automatically the models' hyper parameters (regularization, latent dimensions, .) to find the optimal ones
• Planning optimization: How to plan best notifications to a user across different subjects, days and communications channels

Requirements

Must have:

• internship convention with a French Grande Ecole or a University
• the length of your internship is 5 months or more
• knowledge in applied mathematics (probability, gradient descent optimization.) and algorithmic
• fluency in Python with experience in math libraries (numpy, scipy, sklearn, .)
• knowledge of basic machine learning methods (linear regressions, naive bayes, PCA, kmeans)
• basic knowledge and some experience with one of the Neural Network backends (TensorFlow, PyTorch)

Nice to have:

• knowledge of object and/or functional programming concepts
• understanding the core of the ML libraries
• relation database amp; SQL basics
• experience in data visualization
• contributing to open source software projects (Github, .)

If you believe you have a good profile in Data Science but are missing some of the must haves, please check out other available internship positions.

Please add a cover letter to your application including the information about a date you want to start an internship and its expected duration.

Job Rewards and Benefits