Position: Middle Data Science Engineer (Statistical Computing)
Company: GlobalLogic
Category: Data Science
Experience required: 3y
English level: intermediate
Published: 2021-08-01T00:00:00+03:00

Our Client is a Fortune 500 company and is one of the biggest global manufacturing companies operating in the fields of industrial systems, worker safety, health care, and consumer goods. The company is dedicated to creating the technology and products that advance every business, improve every home and enhance every life.

Requirements:
Collaborate with teams of scientists in activities that will range from modeling and algorithms development to deep analysis research utilizing different approaches and techniques.
You’ll be working with the rest of the development team to specify, design, and build the complete user experience.

Key requirements:
– 3+ years experience in Data Science and/or Machine Learning
– Solid knowledge of Python and R programming languages
– Advanced knowledge and experience in methods for classic machine learning and deep learning, data science, and BI
– Applied statistics knowledge (Design of Experiments (DOE), Multivariate Statistics)
– Solid knowledge of SQL
– Upper-intermediate English (ability to freely communicate and lead a conversation with English-speaking teams)

Will be a plus:
– Degree in the related field
– Engineering experience

Responsibilities:
– Build and execute Monte Carlo simulations take as input statistical parameters that have been discovered from randomized controlled experiments and that generate candidate recommendations for combinations of actions/policies for applying the knowledge generated from the randomized controlled experiment;
– Collaborate with a team of scientists to identify integrate causal and counterfactual inference based structural models (think Judea Pearl) to generate hypotheses that can be tested empirically via randomized controlled experimentation;
– Collaborate with a team of scientists integrating statistical inference methods and techniques into automated systems;
– Apply different approaches for tasks solving;
– Communication with other teams including non-engineering teams.