Data Science Research Assistant

London Business School
London, UK
11 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
£46,553.0
Working hours
Regular working hours

Tech stack

Python (Programming Language) Machine Learning Recommender Systems Web Platforms Information Technology Data Analytics Software Coding

Job description

The Management Science and Operations area at London Business School invites applications for a one-year full-time data science research assistant (RA) position. The RA will contribute to research projects under the supervision of Dr. Ali Aouad on applying data analytics to digital platforms with application to the cultural sector. Previous research in this area includes a collaboration with the Van Gogh Museum, where the research team developed a predictive model of visitor trajectories from multimedia guide data (see working paper here). The idea is to develop a dynamic choice model for cultural experiences (such as a visit to the museum or a session on a streaming platform) and apply it to recommendation systems and algorithmic curation. The RA may support other research projects, including analyzing a food choice dataset in a low-income community. We strongly encourage applications from candidates passionate about data science for social good who wish to pursue a Ph.D. after

Requirements

a pre-doctoral research experience.

Applicants must have completed a BS/MS degree before the appointment in a data science field, which may include computer science, applied mathematics, econometrics, operations research, or a related field. They must demonstrate capabilities for writing code (Python or R), basic knowledge in mathematical modelling, and prior experience using libraries in statistics, machine learning, or operations research.

Benefits & conditions

The position is funded by a research grant. We will offer a competitive salary depending on qualifications and full access to the LBS environment. The RA will benefit from the resources of the MSO community, which includes interactions with faculty and PhD students, mentoring opportunities, access to research seminars, etc.

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