NLP Engineer

Job Requirements

The Purpose of the role

As a key member of the Risk Management Team, he/she will be responsible for providing Natural Language Processing support to help improve our NLP products and create new NLP applications. The NLP Engineer will cater to the risk management function's growing needs to analyze emerging risks across various business lines and geographies.

NLP Engineer responsibilities include transforming natural language data into useful features using NLP techniques to feed classification algorithms. The individual is expected to possess strong statistical knowledge, good understanding of machine learning methods and text representation techniques.

The individual will be expected to work with different workgroups and stakeholders and develop efficient self-learning NLP applications. The individual will be expected to interpret and communicate results with stakeholders in an effective manner.

Responsibilities

• Study and transform data science prototypes
• Design NLP applications
• Work on multiple input data formats and transfer data into a model consumable format
• Select appropriate annotated datasets for Supervised Learning methods
• Use effective text representations to transform natural language into useful features
• Find and implement the right algorithms and tools for NLP tasks
• Develop NLP systems according to requirements
• Train the developed model and run evaluation experiments
• Perform statistical analysis of results and refine models
• Extend ML libraries and frameworks to apply in NLP tasks
• Remain updated in the rapidly changing field of machine learning
• Handle client requests and perform work with little to no assistance
• Gain knowledge and expertise on multiple lines of business
• Use NLP to support various research and analytics assignments

Work Experience

·      Proven experience as an NLP Engineer or similar role

·      Understanding of NLP techniques for text representation, semantic extraction techniques, data structures and modeling

·      Ability to effectively design software architecture

·      Deep understanding of text representation techniques (such as n-grams, bag of words, sentiment analysis etc.), statistics and classification algorithms

·      Knowledge of Python

·      Ability to write robust and testable code

·      Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)

·      Strong communication skills

·      An analytical mind with problem-solving abilities

·      Degree in Computer Science, Mathematics, Computational Linguistics or similar field