As a Data Scientist at Mytraffic, you will be part of a team called Predictive Analytics, responsible for answering business questions vital to our clients based on data.
Mytraffic relies on a large amount of spatial datasets to model urban behaviour, which can be used to make decisions such as: where are my competitors?
Which is the best location for a new site?
Which features are driving my sales?
Is my point of sale under/over performing with respect to its potential?
Our dataset streams come from both internal data (coming directly from our customers for their own use) and external data (retrieved, cleaned and prepared internally at Mytraffic from several sources), which are used to deliver analyses and insights allowing our customers to understand their current and past business situation (descriptive analytics), providing them with models and tools to be able to predict the impact and effects of potential business actions and decisions (predictive analytics) and, at a higher level, recommend them which of those actions and decisions should be taken to maximise their final revenue (prescriptive analytics).
To do so we rely heavily on Python, and more precisely, on three different types of technology: data management and analysis tools (e.g.
Pandas, Matplotlib, Seaborn, Plotnine, Jupyter...), machine learning frameworks (e.g.
scikit-learn, LIME...) and software development oriented libraries (e.g.
Flask, Pytest...).
We are looking for a Data Scientist (at least 2 years excluding internships) passionate about finding, processing and modelling data to solve real world problems.
You would be one of the main points of reference for clients to, given a business problem, figure out how to solve it with the existing datasets, or how to find out those which could be useful to complement the existing ones.
Here are some other things we're looking for:
BS or MS in Computer Science, Engineering, Mathematics, Statistics, Physics or a related field.
The candidate should have a solid foundation in the relevant technical concepts and principles.You have the ability to approach complex problems with analytical capabilities and critical thinking skills, enjoy facing challenging problems, and are able to apply logical reasoning to find solutions.Proficiency in Python programming, including the ability to write functions and classes.
You must be comfortable using Python as your primary programming language and be willing to learn and apply additional patterns and best practices.Experience in analyzing data using Python and common libraries such as Pandas and Scikit-learn.You have an excellent understanding of machine learning techniques and algorithms, such as k-NN, SVM, Decision Trees, Random Forests, XGBoost, etc., although you are aware that the best solution sometimes is the simplest, and you know when to use one or another solution.A good understanding of statistics, including probability theory and common statistical tests.
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