Senior Data Scientist

The AI Solution Delivery team is expanding its capabilities in NLP to build production-grade AI products. We are looking for a Senior Data Scientist to architect, build and deploy complex models for automating customer support operations. You will be responsible for selecting the right technical approaches and ensuring the scalability of our solutions. A proven track record in applying advanced NLP techniques to real-world problems is required.

Lead the entire machine learning model lifecycle, from initial research and hypothesis testing to production deployment and maintenance
Translate complex business goals into well defined data science problems and quantifiable metrics
Design and develop robust, scalable machine learning systems from scratch, including data analysis, annotation, and processing pipelines
Contribute to the overall system architecture and integrate ML models with existing backend services and infrastructure
Monitor and maintain deployed models, proactively identifying and addressing issues like concept drift to ensure consistent performance
Support the development and growth of other team members through mentorship and participation in onboarding programs
Drive continuous improvement by automating repetitive tasks and proposing innovative solutions that lead to significant business impact
Communicate complex technical concepts and findings clearly and concisely to both technical and non technical stakeholders

5 years of professional experience in a data science or machine learning role with deep focus on NLP.
Previous software engineering experience is preferrable.
An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus
Expert level proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit learn).
Deep expertise in classic machine learning and deep learning techniques, with a strong understanding of advanced mathematics relevant to these fields.
Experience with ML system design and MLOps practices for building, testing, deploying, and monitoring models in a production environment.
Proven experience with event systems, deployment environments, and maintaining production services.
Familiarity with technologies for streaming, batch, and async data processing.
Strong understanding of software system design principles and the ability to contribute to architectural discussions.
Experience in experimental design to validate hypotheses and measure the effectiveness of solutions.
A solid grasp of security, risk, and control concepts in a production environment.