Shapr app comes up with a daily batch of people to meet. The underlying suggestion models should make the suggestions that most likely yield to matches (e.g., common interests). To this end, we are now experimenting new methods from machine learning in a data-driven fashion, which leverages issues about data quality. This internship is part of a larger project aiming to ensure high-quality and real-time data flow to the learning models.

Specifically, you will be in charge of implementing a full-stack process for automatic user segmentation. Segmentation aims at characterizing users with a score or a discriminant category depending on their job’s title, experience, and other profile information. The task can naturally be cast as a (supervised) machine learning task involving the prediction of some category (segment) from a bulk of structured and unstructured data.

As part of the Data Science team, you will be in tight collaboration with the Data Science Lead and the CTO. You mission will include but not limited to:

• Experiment a set of supervised learning models for automatic user segmentation (linear/logistic models, kernel-based models, latent-variable models…)
• Perform and evaluate relevant data preprocessing
• Evaluate the efficiency of the predictive models for real-time segmentation (quality, efficiency)
• POC: unsupervised learning models for user segmentation

As part of the data science process, you’ll be expected to implement some data access/data cleaning routines. A brief literature review will also be performed previous to the analytics.

Keywords: Machine learning, Text mining, Profiling, Categorization, Scoring, Descriptive statistics, Linear models.

You are a last-year candidate or a graduate of a Master-of-Science program, with a strong background in Applied Mathematics/Statistics. You are familiar with machine learning methods and at least one scripting language (R/Python). This offer is for you! Join Shapr and together, let’s make the most of your energy.

Perks include

• Competitive salary based on your experience
• Half-fare restaurant/travel tickets
• Schedule: full-time, 6-month, starting asap
• Location: near Avenue des Champs Elysées, Paris (RER-A / Metro 1-2-6)

Get in touch

• Mohamed Dermouche, Data Science Lead: mohamed.d@shapr.net
• Cyril Ferey, CTO: cyril@shapr.net