Natural Language Processing Job Description

Natural language processing (NLP) is a subset of artificial intelligence (AI) that focuses on creating machines and software capable of understanding and interpreting human language, as well as generating text that resembles it. Interested in joining this impactful AI field? Here's what you can expect from an NLP job, at a glance:

• NLP professionals earn a higher-than-average salary. According to Glassdoor, NLP engineers earn a median total pay of $161,000 [1].

• The job duties of an NLP professional vary based on their title, such as NLP engineer, data scientist, or AI developer.

• To enter the field, you'll need to develop skills in machine learning, programming, and statistical analysis, among other things.

Below, you'll explore common duties and responsibilities found in natural language processing job descriptions along with typical roles in the field. Afterward, if you want to build your NPL skills, you might consider enrolling in Packt's Natural Language Processing with Real-World Projects Specialization.

Natural language processing job description

Your exact duties as an NLP professional will vary based on your specific job title. Nonetheless, some common responsibilities you'll handle 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 also essential for NLP professionals. 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. 

Natural language processing jobs

There are many specialized roles in NLP. Some of those you may consider pursuing include:

1. 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 a 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 earn a higher-than-average salary. According to Glassdoor, NLP engineers earn a median total pay of $161,000 [1]. 

2. 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 a doctorate. The data science job outlook is highly favorable: the field is expected to see a 36 percent growth rate between 2023 and 2033, and the median annual pay for a data scientist is $108,020 [2]. 

3. 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]. 

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. 

1. 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. 

2. 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. 

3. 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. 

Learn more about NLP and AI on Coursera

NLP is an exciting technology that has made its way from the domain of computer science to a variety of industries and sectors. With Coursera Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs—just check the course page to confirm your selection is included.  

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