Вакансия Data Science Engineer (NLP expertise)

Skills

• NumPy
• SciPy
• Pandas
• scikit-learn
• TensorFlow
• Keras
• PyTorch
• NLP
• LLM
• AWS
• Azure
• GCP
• SQL

Описание вакансии

About the position

Quantum is expanding the team and has brilliant opportunities for Data Science Engineers with NLP expertise. The client is a technological research company that utilizes proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in all languages. Our mission is to bridge the knowledge gap in the investment world and empower investors of all types to become “super-investors.” Through our generative AI technology implemented into brokerage platforms and other financial institutions’ infrastructures, we offer instant fundamental analyses of global stocks alongside bespoke investment strategies, enabling informed investment decisions for millions of investors worldwide.

Must have skills:

• At least 3 years of commercial experience in DS
• Strong knowledge of linear algebra, calculus, statistics, and probability theory
• Knowledge and experience with algorithms and data structures
• Experience with Machine Learning libraries (NumPy, SciPy, Pandas, ScikitLearn)
• Experience with at least one of the Deep Learning frameworks (Tensorflow, Keras, PyTorch)
• Knowledge of modern Neural Networks architectures
• Experience in Natural Language Processing, including LLMs
• Experience with Cloud Computing Platforms (AWS, GCloud, Azure)
• Docker practical experience
• Experience with SQL
• Strong knowledge of OOP
• At least an Upper-Intermediate level of English (spoken and written)

Nice to have skills:

• Experience with MLOps solutions
• Basic understanding of Big Data concepts
• Experience in classical Computer Vision algorithms
• Participation in Kaggle competitions

Your tasks will include:

• Full-cycle data science projects
• Data analysis and data preparation
• Development of NLP/Deep Learning / Machine Learning; Developing models and deploying them to production
• Sometimes, this will require the ability to implement methods from scientific papers and apply them to new domains