Natural Language Processing Job Description

As more industries adopt artificial intelligence (AI) technology, natural language processing (NLP) is no longer just a niche subfield of interest for computer programmers. NLP is also key to modern AI’s capacity for understanding and interpreting human language and generating human-like text in response to user queries. 

A natural language processing job description may include myriad applications, such as powering GPS systems, improving search engine capabilities, and potentiating voice-operated digital assistants. As such, those with NLP skills are in high demand. 

What is natural language processing?

NLP is a machine learning (ML) technique by which programmers imbue AI with a humanlike capacity for language learning. You can use NLP to teach AI to understand human-generated text and speech. Over time, the AI platform learns to provide realistic, helpful, humanlike responses to a variety of user queries. 

NLP powers generative AI, with chatbots resulting from one particularly common iteration. You can also use NLP to streamline workflows and increase workplace efficiency and productivity. 

NLP is, in part, an offshoot of computational linguistics, a discipline in which you learn to describe how people and machines collect and analyze data to understand human language better. Two main types of language analysis modalities in computational linguistics are: 

• Syntactical: Determines the meanings of words in terms of syntax and prescriptive grammar.

• Semantical: Determines the meanings of words as drawn from syntax—that is, as parts of a whole.

Further, NLP sophistication depends upon an NLP system’s capacity for understanding pragmatics, which is its ability to apprehend the contextual meaning of a sentence. Because human language is both literal and nonliteral, the context in which you use a word or phrase can change the meaning of that word altogether, as can the tone of a seemingly straightforward sentence. NLP allows AI to understand such ambiguities better. 

How is NLP used? 

You can take advantage of a variety of NLP and AI career paths. Various types of professionals use NLP in many ways, including sentiment analysis, machine translation, and information retrieval. 

Sentiment analysis

Sentiment analysis is a way to help you determine whether customer sentiment regarding a product or service is generally positive, negative, or neutral based on key emotion words (love, hate) that fit (generally speaking) into one or another of said respective categories.

Using NLP-powered AI for sentiment analysis has a variety of benefits. It removes human subjectivity from the equation, allows for the scaling of sentiment analysis due to AI’s ability to analyze large data sets quickly, and can analyze new data in real time. All this helps you develop better products and services based on knowing, to a granular degree, what people like and dislike about previous offerings. 

Machine translation

NLP allows an AI program to translate between languages quickly, sometimes in seconds. It reduces the overall cost of translation, and newer iterations of NLP-based machine translation software continue to become more accurate. 

Via its combination of ML and rule-based language modeling, NLP can help AI understand multiple languages at once, further speeding up the translation process. 

Information retrieval

As an NLP professional, you may need to search a variety of text types for detailed information. You might work with: 

• Text

• Images

• Video

NLP-powered AI helps you to quickly parse enormous data sets of all varieties for key information buried amid unhelpful details and digressions. Industries and sectors utilizing NLP-powered information retrieval include the legal, medical, and finance fields. 

Types of NLP roles

Many specialized roles utilize NLP, such as: 

NLP engineer

An NLP engineer is an AI and ML expert who designs and programs NLP systems for various use cases and analyzes and refines said systems as business objectives require. As an NLP engineer, you might work alongside other professionals such as:

• Data analysts

• Designers

• Front-end developers

You’ll likely need a bachelor’s degree in data science, engineering, or computer science to be an NLP engineer. Some companies may require you to have a master’s or doctorate. Occasionally, no degree is necessary—NLP engineers can start at some companies as interns or apprentices before moving into full-time professional positions. 

NLP engineers are in high demand: experts project job growth of 26 percent between 2023 and 2033, and the average NLP salary is around $145,080 annually [1]. 

Data scientist (NLP-focused)

Data scientists work to make sense of large data sets, extracting key information and determining patterns from them to help businesses make better-informed data-driven decisions. Many data scientists use AI in their day-to-day work. 

Data scientists combine skills from various fields, such as: 

• Technology

• Business

• Communications

• Mathematics

NLP plays a role in modern data science. NLP is, after all, a combination of data science, computational linguistics, ML, and deep learning. As a data scientist, your core ability to glean key information from large data sets allows you to help programmers develop more accurate NLP models. 

To become a data scientist, you’ll need at least a bachelor’s degree in computer science, statistics, or mathematics. Some companies may require you to have a master’s or doctorate. The data science job outlook is highly favorable: the field will likely see a 36 percent growth rate between 2023 and 2033, and the median pay for a data scientist is $108,020 per year [2]. 

AI developer (Language technology-focused)

An AI developer’s responsibilities overlap somewhat with those of coders and software engineers, albeit with a focus on developing AI technologies. AI developers focused on language technology and NLP is currently in demand as the use of NLP-powered chatbots, digital assistants, and even self-driving cars continues to rise. 

AI developers design and implement AI-based business solutions. They also train various employees and stakeholders in practical AI usage. 

Generally, to get a job as an AI developer, you must have a bachelor’s degree in computer science, engineering, artificial intelligence, or robotics. Some companies may require more advanced formal education. The average salary for an AI developer with a language technology focus is $130,160 [3]. 

Natural language processing duties and responsibilities

Duties and responsibilities involved in NLP include: 

• Developing and implementing NLP algorithms

• Processing and analyzing text data

• Evaluating model performance

• Building language models

To accomplish these tasks, you’ll want to understand important practical NLP concepts such as: 

• Part-of-speech tagging: NLP tags words in a sentence based on their usage—e.g., verbs, nouns, adjectives, etc. This allows it to learn human language in a pattern-like way. 

• Word sense disambiguation: You will need to be able to program NLP to understand the difference between word usage. For example, “dog” is a noun—it refers to a class of animal—as well as a verb meaning “to follow persistently” (“dog my steps”). 

• Speech recognition: This is how NLP turns audio into text. With careful programming, an NLP program can learn to understand intonation, bespoke grammar, even accents, and it can transcribe spoken language into nuanced text. 

• Named-entity recognition: This is the process by which NLP identifies proper nouns in a sentence—i.e., names of entities such as companies, people, and places. It then determines its semantic relationship to develop its pattern-based language learning capability further. 

Essential NLP skills

Key skills for NLP professionals include: 

• Experience with programming languages such as Python, Java, and R

• Knowledge of various ML methodologies

• Knowledge of ML libraries and frameworks (NLTK, SpaCy, etc.)

• Coding abilities

• Facility with statistical analysis

Various workplace skills are essential for NLP professionals as well. These include: 

• Critical thinking

• Collaboration

• Time management

• Conflict management

• Creativity

Specific qualifications will vary based on your precise role and the company for which you work. 

Learn more about natural language processing with Coursera.

NLP is an exciting technology that has made its way from the domain of computer science to a variety of industries and sectors. Learn more about NLP with Coursera. Standford and DeepLearning.AI offer the Machine Learning Specialization, where you’ll build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods.