Natural Language Processing Job Description

Natural language processing (NLP) professionals are important when it comes to enabling today’s artificial intelligence (AI) technologies to understand, decipher, and generate human language. NLP-powered devices are increasingly common, and demand for NLP professionals exists in a wide variety of industries and sectors. 

Learn about NLP, the types of NLP roles out there, what skills you need to work as an NLP professional, and more. 

What is natural language processing?

NLP is a specialized sub-discipline within the field of AI. The goal of NLP professionals is to teach human language to computers via a method that resembles human learning. NLP powers the large language models (LLMs) that allowed for the creation of generative AI interfaces such as ChatGPT as well as digital assistants such as Amazon’s Alexa.

NLP combines the following techniques: 

• Machine learning (ML): Machine learning involves training computers via sample data to get better at the nuances of human language. 

• Deep learning: Via neural networks, computers learn to classify data and recognize patterns in it in a humanlike way.

• Computational linguistics: This allows programmers to use syntactical and semantical analysis techniques to power NLP’s conversational ability.

NLP professionals use automated processes to examine and classify enormous unstructured data sets faster than any individual programmer can. This makes NLP a key aspect of modern AI technology. 

In addition to the above, common NLP applications include: 

• Document summarization: Offers automated summaries of large text files of all types 

• Machine translation: Automated translation between languages in text or speech form 

• Sentiment analysis: Identifies mood and emotion in text, such as in a customer review 

• Speech-to-text conversion: Can convert vocal instructions into written text or written text into voice commands 

Types of NLP roles

Because NLP has such vast capabilities, you’ll find a variety of NLP roles available. Examples include NLP engineer, data scientist, and AI developer.

NLP engineer

NLP engineers are AI professionals who help design NLP-based applications that perform a variety of language functions. They help AI learn to recognize speech patterns, define the data sets NLP programs learn on, refine learning algorithms, and program appropriate algorithms to get the most out of a program’s NLP abilities. 

A wide variety of companies seek NLP engineers. Industries and sectors where NLP engineers are in demand include: 

• Defense

• Education

• Entertainment

• Finance

• Government

• Health care

• Information technology

• Marketing

• Retail

As an NLP engineer, you’ll generally need a bachelor’s degree in computer science, data science, or engineering. The average annual salary for an NLP engineer is $123,604 [1]. 

Data scientist specializing in NLP

Data scientists—some of whom specialize in NLP—create predictive algorithms and models in order to analyze large stores of data for business purposes. Data scientists apply insights from patterns gleaned from this data to assist stakeholders in making key business decisions. For this reason, they are in high demand. 

Data scientists possess facility with: 

• Databases

• Data storytelling

• ML

• Programming languages

• Software engineering

• Statistical analysis

Because data scientists help steer business decisions, they must also possess a variety of workplace, or nontechnical, skills in addition to the technical skills listed above. These include: 

• Analytical and critical thinking

• Business savvy

• Communication skills

• Curiosity

Data scientists need a bachelor’s degree—sometimes a master’s or doctoral degree—in computer science, mathematics, statistics, or a related field. The average annual pay for a data scientist is $119,742 [2]. 

AI developer focused on language technologies

AI developers create and program training methodologies that power AI-based language technologies. In this role, you work with data to help stakeholders develop AI-based business solutions; you also train employees on using said AI technologies.

AI developers are in demand due to the fact that advanced language technologies power a number of increasingly common applications, such as: 

• Chatbots

• Disease-mapping technologies

• Self-driving cars

• Smart assistants

• Social media monitoring

To become an AI developer, you’ll need a bachelor’s degree with a focus in artificial intelligence, computer science, engineering, robotics, or a related field. The average annual salary for an AI developer focused on language technologies is $112,364 [3]. 

Essential NLP skills

NLP professionals need a variety of skills, including programming languages, libraries and frameworks, models, and various core concepts. 

Programming languages

Common programming languages NLP workers use include: 

• Python

• Lisp

• R

• Prolog

• JavaScript

• C++

• Java

• Scala

• Julia

NLP techniques and concepts

You’ll need to understand a variety of NLP techniques and core concepts such as: 

• Lemmatization: Removing the endings of words and leaving behind their common root 

• Named entity recognition: Extracting words and phrases and placing them into entity classes such as “people,” “places,” or “symbols” 

• Part-of-speech tagging: Classifying and tagging parts of speech in a sentence—nouns, verbs, adjectives, etc.

• Tokenization: Breaking sentences up into individual word tokens while discarding grammar

Frameworks and libraries

You’ll need close familiarity with NLP frameworks and libraries such as: 

• TensorFlow

• PyTorch

• spaCy

• NLTK

• Gensim

Statistical modeling

NLP professionals must understand the following statistical models:

• Bidirectional: Models programmers use to analyze text from the beginning of a sentence to the end and vice-versa, which teaches a model to understand words in several contexts.

• Continuous space: This takes place on the neural network level to analyze data sets with larger lexicons.

• Exponential: With an exponential model, you assign a score to the probability of different words coming one after the other and program into the model parameters to make the program learn more accurately.

• N-gram: This is a probabilistic model where “n” is the number of words in a string of words or syllables. 

• Unigram: This is a model of a single word or phrase in and of itself (n = 1) utilized primarily for information retrieval. 

Qualifications and experience requirements

Typical qualifications for NLP-based jobs include a bachelor’s degree in: 

• Computer science

• Data science 

• Engineering 

• Mathematics

• Statistics 

You may choose to earn a certificate in lieu of a degree where possible, or work an internship if the job you want requires more experience than you currently have. 

Key competencies for success

Key competencies for NLP professionals include the following:

• Creativity

• Critical thinking

• Leadership 

• Self-motivation

• Time management

• Collaboration

• Communication

Example NLP job description

NLP Engineer wanted for leading company in the AI space. 

Essential tasks: Design, train, and evaluate new NLP models.

Skills needed: 

• Ability to write code using Python

• Good communication skills

• Knowledge of software architecture

• Machine learning methodologies

• Statistical analysis

• Text representation techniques

Industry variations in the NLP role

NLP roles vary across industries and sectors. For example: 

Finance: NLP engineers analyze customer sentiment and help generate accurate predictive economic reports for financial companies. 

Marketing: NLP professionals work with sentiment analysis in order to optimize the use to which companies put customer feedback.

Tech: Conversational AI engineers develop NLP abilities and voice recognition technology in chatbots and virtual assistants. 

Well-known companies in a variety of fields hire NLP-based roles. Examples include: 

• Amazon

• Etsy

• Google

• IBM

• Lockheed Martin

Career path and growth opportunities

Career paths and growth opportunities for NLP professionals exist in profusion. For instance, you may start out with a title such as: 

• Data analyst

• NLP research engineer

• NLP software developer

As you develop your technical skills alongside a facility for product and people management, you may find yourself qualified for positions such as: 

• Conversational AI developer

• NLP engineer

Eventually, you may be able to move up to director-level positions, including: 

• Director of AI product

• Director of data science

• Director of NLP engineering

Note that your NLP career growth path will depend on factors such as your level of education, experience, and the specifics of past NLP jobs you’ve held. 

Learn more about NLP with Coursera

Regardless of your exact NLP job description, if you’re interested in NLP you have the chance to work at the vanguard of technology, linguistics, and computer science in order to create more advanced language technology applications for daily use. 

Learn more about NLP with Coursera. The IBM Machine Learning Professional Certificate, as well as the Machine Learning Specialization from Stanford and DeepLearning.AI, can help you develop the practical skills necessary for programming today’s exciting NLP-based AI interfaces.