General Information

Team:

Sonata

Close date:

Friday, 01 July 2022

Working pattern:

Full time

Contract Type:

Permanent

Location:

London

Department:

25 - Development

Description & Requirements:

Position Purpose
In this role as a Research Scientist at Bravura Solutions, you will actively work with Bravura’s Machine Learning Engineers to deliver state-of-the-art models to different software environments. With a strong focus on deep learning, you will draw on your expertise from a variety of techniques such as clustering, anomaly detection, dimensionality reduction, transfer learning and reinforcement learning to meet ambitious goals.
Part of our broader Bravura Innovation framework, the team is laser-focused on delivering new automation offerings to clients across different algorithmic domains, initially targeting NLP, Computer Vision and multimodal architectures. 

Main Activities
  • Research state-of-the-art ML literature across domains, scientifically analyse their applicability on our datasets and adapt and implement those solutions
  • Define scientific evaluation strategies for AI models, focusing on image recognition and NLP
  • Create research prototypes and transition them to products in collaboration with the ML engineering team to exhibit quantifiable business benefits
  • Identify new AI based opportunities within Bravura business domain 
  • Actively publish scholarly articles within the scientific community through conferences and journals

Key skills
  • Excellent coding skills of at least one language among Python, Java and C/C++
  • Strong skills in Machine Learning frameworks: Tensorflow, Pytorch, Scikit-Learn
  • Familiarity with cloud platforms for data analysis and model training
  • Familiarity with relational databases

Qualifications and Experience
  • Strong Academic or Industrial experience in core deep learning research, not limited to a specific domain
  • PhD in machine learning or equivalent practical experience with a PhD in deep learning preferable
  • Knowledge in handling model training at big data scale on distributed hybrid clusters
  • At least 1 year’s experience working in a post-doctoral role