Expert Data Scientist

Descrição do trabalho

Overview

We are a Big Data Competence Center combining various clients, interesting projects, and activities to grow your professional skills.

We act in various business domains and work with top-range clients. Our team highly supports employees' freedom, independence in decision-making, and the desire to deeply understand client requests and see the root of the problem.

Our goal is to provide the best technical excellence services for our clients by collecting the best engineering practices and uniting them into one knowledge base.

• Strong relationships between us and our clients allow us to provide perfect matches between client needs and your professional interests.
• Requirements
• Strong Python and SQL knowledge and experience
• Experience with model implementation and tuning using popular Machine Learning frameworks such as PyTorch, Keras, TensorFlow
• Ability to understand machine learning development and deployment processes
• Solid experience with SageMaker and all its features (e.g., Sagemaker Pipelines)
• Ability to create and adjust SageMaker pipelines for the end-to-end ML development cycle
• Experience with Deep learning models for signal and speech processing
• Bonus Requirements
• Experience with Deep learning models for Natural Language Processing domain
• Experience setting up multi-GPU infrastructure for model training
• Experience with general DevOps practices and principles
• Responsibilities
• Working and professionally communicating with the customer's team
• Taking up responsibility for delivering major solution features
• Participating in requirements gathering & clarification process, proposing optimal architecture strategies, leading the data architecture implementation
• Developing core modules and functions, designing scalable and cost-effective solutions
• Performing code reviews, writing unit and integration tests
• Scaling the distributed system and infrastructure to the next level
• Helping the client's research team with the implementation, training, testing, and tuning of deep learning models
• Developing AWS machine learning infrastructure to support and maintain model serving and training
• Providing guidance and best practices to effectively and securely leveraging the Amazon SageMaker service for interaction with a model
• Developing reusable project templates to create the infrastructure for MLOps solutions for CI/CD of ML models
• Collaborating with the research team to identify best-fit models and open-source alternatives related to language and signal processing to get the best possible result