Software Engineer II
Have you always been top of your job or class? Do you always pick things up faster than everyone else? Is your biggest fear that your job will not stretch you and will get monotonous? Is your ideal job description: “do whatever it takes to win?” Is your approach to team-work: “win together or die together”?
If your answer to these questions is: “of course, isn’t everyone like that?” Then the answer is; “no, they are not, but we are, and we may have the perfect job for you!”
About our Business
LexisNexis Risk Solutions provides customers with solutions and decision tools that combine public and industry-specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. We use the power of data and advanced analytics to help our customers make better, timelier decisions. By bringing clarity to information, we ultimately help make communities safer, insurance rates more accurate, commerce more transparent, business decisions easier, and processes more efficient. You can learn more about LexisNexis Risk at the link below:
https://risk.lexisnexis.com/
.
About our Team
Our team is a small R&D team serving a large, extremely successful, socially responsible, big data business. Generally, the business is a well-oiled machine but sometimes they need a new part or a bit more oil; that’s when we get called in. We really will do whatever it takes but generally our solutions focus upon domain specific languages and automated code generation. We are very high functioning and considered an elite group.
About the Role
We are seeking a talented NLP Engineer with some experience of industry experience, coupled with some familiarity of with software development in Java. The ideal candidate will possess a deep understanding of Natural Language Processing (NLP) techniques and methodologies, along with a proven track record of successfully implementing NLP solutions. As well as the aptitude to integrate NLP solutions a java technology stack. If you are passionate about leveraging NLP to tackle real-world challenges and possess some understanding of Java development.
Many people on the team have education up to doctorate level but that is not a requirement. Please re-read the opening paragraph, it is not a catch phrase or a mission statement. It actually IS who we are. If this resonates with you this job needs serious consideration; if it does not – you will hate it. We consider ourselves to be “One Team” and we live by that motto.

Requirements:

• Software Engineering experience
• MS Engineering/Computer Science or equivalent experience required.
• Design, development, and implementation of NLP algorithms and models using Java-based technologies.
• Leverage machine learning and deep learning techniques to extract insights and derive meaning from unstructured text data.
• Some knowledge of Java.
• Develop efficient algorithms and pipelines for preprocessing, parsing, and analyzing large volumes of text data.
• Evaluate the performance of NLP models and algorithms using appropriate evaluation metrics.
• Good knowledge in algorithms to find and implement tools for NLP tasks.
• Knowledge of NLP libraries and frameworks
• Fine-tune and optimize NLP models for accuracy, efficiency, and scalability in production environments.
• Stay abreast of the latest advancements in NLP research, methodologies, and tools.
• Drive innovation by proposing and implementing novel approaches to address complex NLP challenges.
• Strong communication skills

Responsibilities:

• Develop NLP systems according to requirements
• Maintain NLP libraries and frameworks
• Design and develop natural language processing systems
• Define appropriate datasets for language learning
• Use effective text representations to transform natural language into useful features
• Train the developed model and run evaluation experiments
• Find and implement the right algorithms and tools for NLP tasks
• Perform statistical analysis of results and refine models
• Constantly keep up to date within the field of machine learning
• Implement changes as needed and analyze bugs.

Women in Tech:

LexisNexis Risk is supportive of women in Technology and has been a founding signature for the Tech Talent Charter. We have the following initiatives in place to support women in technology:

• Mentoring scheme for women in technology
• Women’s network forum and regular run events for schools about careers in technology to inspire the next generation of girls in technology.

Culture and Benefits:
Learn more about the LexisNexis Risk team and how we work
here
.

LI-TL1#LI

At LexisNexis Risk Solutions, having diverse employees with different perspectives is key to creating innovative new products for our global customers. We have 30 diversity employee networks globally and prioritize inclusive leadership and equitable processes as part of our culture. Our aim is for every employee to be the best version of themselves. We would actively welcome applications from candidates of diverse backgrounds and underrepresented groups.
We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form:
https://forms.office.com/r/eVgFxjLmAK
, or please contact 1-855-833-5120.
Please read our
Candidate Privacy Policy

• Develop NLP systems according to requirements
• Maintain NLP libraries and frameworks
• Design and develop natural language processing systems
• Define appropriate datasets for language learning
• Use effective text representations to transform natural language into useful features
• Train the developed model and run evaluation experiments
• Find and implement the right algorithms and tools for NLP tasks
• Perform statistical analysis of results and refine models
• Constantly keep up to date within the field of machine learning
• Implement changes as needed and analyze bugs