Statistician/Data Scientist

Dragontail Systems is the leading B2B company of revolutionary optimization software for the food and delivery industry with a global presence.

We are proud to be part of Yum! Brands, a company with over 55,000 restaurants in more than 150 countries and territories primarily operating the company’s restaurant brands – KFC, Pizza Hut, Taco Bell and The Habit Burger Grill.

What you get to do in the role:

You will be working on the flagship product of Dragontail called “Algo”. It is a scheduling and optimization engine that synchronizes the preparation of orders with takeaway and deliveries for reducing delivery and waiting times.

You will lead the initiative on how to utilize the data to bring additional value to the business. You will work on the entire lifecycle including data collection and cleansing, feature engineering, modelling, preparing for deployment, monitoring and analyzing the model’s performance.

Your daily work will include:

Explore and visualize data, identify anomalies and unusual events.
Build statistical models for parameters required for the optimization and scheduling engine.
Use the data to investigate human behavior in industrial kitchens.
Build predictive models for delivery times using time series and tabular data.

You will work closely with software developers building the optimization engine, product owners and the operation team and you will be supported by the data science team.

Requirements:

Bachelor of science/engineering/software degree, an advantage to MA in the field.
3-5 years analyzing data (time series/tabular) and building statistical models
3 years of experience on training and evaluating regression and classification models e.g. polynomial regression, decision trees, SVM
Experience with shell scripting, AWS/Azure/GCP, SQL, Linux - advantage
Programming experience: Python – Must, (R, SQL, Julia, Go, other languages are also well regarded)
Knowledge and experience on predictive analytics e.g. labor and sales forecast - advantage
Knowledge on Bayesian modelling – advantage