Natural Language Processing Career Path: Complete Guide 2024

Becoming an NLP or Natural Language Processing Engineer involves a combination of the following: 

• Educational Qualifications: A bachelor’s degree in Computer Science, Data Science, or a related field.
• Programming Skills: Proficiency in programming languages, including Python, Java, or C++. Familiarity with NLP libraries and tools, such as NLTK, spaCy, and Transformers.
• Data Modeling and Engineering: Skilled in data modeling and engineering to work with large datasets.
• Building a portfolio: Showcase your skills by creating a portfolio of NLP projects and contributing to open-source NLP projects.
• Networking: Connect with professionals and build a network by attending NLP conferences, workshops, and meetups. 
• Applying for jobs: Apply for NLP engineer positions in companies working on NLP applications, like chatbots, sentiment analysis, etc.
• NLP Specialized certifications: Pursue NLP-specific certifications and stay updated on the latest NLP research and trends.  

The skills required to become a Natural Language Processing Engineer include: 

• Programming Skills 
  NLP engineers require proficiency in recession-proof tech skills like Python, R, and SQL programming languages. They ought to be able to work with a lot of data, write well code, and be familiar with libraries for manipulating and analyzing data like Pandas, Numpy, and Scikit-learn. Here are some courses to help you learn the Python programming language:
  • Python Fundamentals
  • AI Programming with Python
     
• Machine Learning
  NLP engineers should be familiar with ML advantages, algorithms and methods. They ought to be able to adjust hyperparameters to boost performance, train and evaluate models, and select appropriate algorithms for various problems. Here are some courses to help you learn ML:
  • Virtual Reality Specialization by Coursera
  • IBM Machine Learning Professional Certificate
     
• Data Visualization
  NLP engineers ought to be able to communicate insights through data visualization effectively. They should be able to create visualizations that effectively convey complex data and be familiar with visualization libraries like Matplotlib and Seaborn. Knowledge of dashboarding tools like Tableau/Qlikview can be a great add-on. Here are some courses to help you study data visualization:
  • Data Visualization Nanodegree Program
     
• Data Engineering
  Data warehousing, ETL processes, and database design are all examples of Data engineering concepts that NLP engineers should be familiar with. They ought to be able to work with a lot of data and make the most of data storage and retrieval. Here are some courses to help you study data engineering:
  • IBM Data Engineering Professional Certificate
  • Data Engineering for Everyone 
     
• Linguistics
  NLP engineers must have a solid understanding of linguistics. They should be familiar with morphology, syntax, semantics, and the structure and syntax of natural languages, as they will sometimes need to deal with multiple languages at a time.
   
• Text Processing
  Text processing techniques like tokenization, stemming, and lemmatization should be familiar. NLP engineers must prepare raw text data for analysis and pre-process it.  
   
• Feature Engineering
  NLP relies heavily on feature engineering, and you should be able to deduce meaningful features from text data. You should be familiar with word embeddings, TF-IDF, and bag-of-words strategies.
   
• NLP Algorithms
  NLP engineers must be familiar with various NLP algorithms, such as named entity recognition, sentiment analysis, part-of-speech tagging, and topic modeling. They should be able to select appropriate algorithms for different tasks and evaluate their performance. 
   
• Deep Learning
  Deep learning is becoming increasingly important in NLP, and you should be familiar with deep learning frameworks such as TensorFlow and PyTorch. You should be able to design and train neural network models for NLP tasks such as language modeling, machine translation, and speech recognition. Here are some courses to help you study deep learning:
  • Deep Learning Nanodegree Program 
  • Neural Networks and Deep Learning 
     
• Knowledge of NLP Tools & Libraries
  You should know various NLP libraries and tools, including NLTK, Spacy, and Gensim. These tools should allow you to preprocess text data, extract features, and perform NLP tasks. 
   
• Domain Expertise
  Knowledge of a specific domain, such as financial jargon or medical terminology, is often required for NLP tasks. You ought to have the option to work with space specialists to figure out the unique circumstance and foster NLP arrangements that are custom-fitted to explicit spaces. Some courses that can help you to advance in the field of NLP are mentioned below:
  • NLP Training Course
  • Advanced NLP with Spacy
  • Getting Started with Natural Language Processing in Java

Building an NLP project portfolio is imperative for every NLP engineer. Writing real-world NLP projects is the best way to sharpen skills. It helps in transforming theoretical knowledge into practical experiences. It translates into a professional advantage when looking for employment. 

• Sentiment Analysis
  This is a widely used application of NLP that involves analyzing the sentiment of a given text. The project could involve developing a model to classify whether a text is positive, negative, or neutral. You can use different machine learning algorithms such as baseline LSTM, RNNs, etc., sequence models. The movie sentiment analysis would be a good start present on Kaggle for this.
   
• Named Entity Recognition
  Another popular use of NLP is Named Entity Recognition (NER), in which entities like people, places, and organizations are identified and extracted from a text. Using methods like Conditional Random Fields (CRF) or Bidirectional Encoder Representations from Transformers (BERT), you can work on a project that involves building a model that can recognize named entities in a given text. Python has an implementation for CRFSuite which can be used directly. 
   
• Machine Translation
  This is the process of automatically translating text from one language to another. You can work on a project that involves building a machine translation system using techniques such as sequence-to-sequence models, Attention Mechanisms, or Transformer models.  
   
• Text Summarization
  This involves creating a shorter version of a given text while retaining the essential information. You can work on a project that builds a model to summarize a given text using techniques such as extractive (constructing new sentences for a summary) summarization or abstractive (selecting important sentences from the document without changing anything) summarization.
   
• Chatbot
   A chatbot is a conversational agent that can understand and respond to natural language queries. You can work on a project that involves building a chatbot using NLP techniques such as Natural Language Understanding (NLU) and Natural Language Generation (NLG).

These projects will help you gain hands-on experience and showcase your skills and expertise to potential employers. It is also important to remember that the project should be challenging and relevant to the industry you are targeting. 

• Data Scientist or Related Professional
  It is the easiest for you to become an NLP engineer as you already know most of the basics and possess hands-on experience. You need to work on real-world NLP projects to get exposure. You can transition to becoming an NLP Engineer even if you already have a career as a Senior Data Scientist.
   
• Software Engineer or IT Sector Professional
  The world of data science, especially NLP can be new to you but coding is not. Hence, Software Engineers or IT professionals can get started by acquiring the above-mentioned skills you are missing, starting with one of the major projects and looking for internal mobility within the organization.
   
• Graduate or Postgraduate with a degree in Data Science or a Related Field
  The only thing you are missing is work experience in a real-world project. Landing your first data science job, especially in the NLP field is difficult without prior experience. So, pick-up some major projects, and maintain a Kaggle profile. Working on research papers is also a big plus in landing an NLP job.
   
• Non-IT Sector or Graduate with no major in Computer Science
  Unfortunately, you will need to start everything from scratch. Taking up some online courses can be a good starting point that can introduce you to programming and basic Data Science skills and then eventually NLP. We suggest you start exploring Kaggle as well.

Here is a list of the top Natural Language Processing or NLP certifications to build a career as an NLP Engineer: 

Natural Language Processing Specialization by Coursera

This course prepares you to design NLP applications for question-answering and sentiment analysis. It will teach you to develop language translation tools, summarize text, and build chatbots. This course is designed and taught by experts in NLP, machine learning, and deep learning.

Advanced NLP with SpaCy by Datacamp

This cover will teach you how to use spaCy, an NLP Library in Python, to build advanced natural language understanding systems using rule-based and machine-learning approaches. 

Feature Engineering for NLP in Python by Datacamp

This course covers techniques for extracting useful information from text and processing them into a format suitable for applying ML models. It will teach you about POS tagging, named entity recognition, readability scores, the n-gram and tf-idf models, and how to implement them using scikit-learn and spaCy. After the course completion, you can engineer critical features from any text and solve some of the most challenging problems in data science.

Natural Language Processing in TensorFlow by Coursera

This course will teach you the best TensorFlow practices, and you will build NLP systems using it. It will also cover how to process text, including tokenizing, and represent sentences as vectors. This course involves applying RNNs, GRUs, and LSTMs in Tensorflow. You must be familiar with Python programming language before pursuing this certification. 

Natural Language Processing in Python by Datacamp

This course teaches the core NLP skills required to convert data into valuable insights. It will also cover how to automatically transcribe TED talks and introduce popular NLP Python libraries such as NLTK, scikit-learn, spaCy, and SpeechRecognition. 

The average salary for an NLP Engineer in India is ₹8,00,000. The salary estimates are based on 176 latest salaries from various NLP Engineers across industries.

Here’s how much NLP Engineers earn in different regions:

United States of America 

The annual salary for an NLP engineer in the US ranges from approximately $95,000 to $160,000, according to Glassdoor, LinkedIn, Ambition Box, etc. The following is a breakdown of NLP engineer salaries based on their experience level. 

• Entry-level NLP engineers with less than 2 years of experience earn between $80,000 and $120,000 annually.
• Mid-level NLP engineers with 2-5 years of experience earn between $100,000 and $150,000 annually.
• Senior-level NLP engineers with 5 or more years of experience earn between $130,000 and $200,000 annually.

India 

In India, an NLP engineer can expect to earn anywhere from ₹ 500,000 - ₹ 2,000,000 annually. Based on experience, the following is a breakdown of NLP engineer salaries: 

• Passage level NLP Specialist (0-2 years of involvement): ₹400,000 to ₹ 800,000 every year.
• Mid-level NLP Designer (2-5 years of involvement): ₹ 800,000 to ₹ 1,500,000 yearly.
• Senior-level NLP Designer (5+ years of involvement): ₹ 1,500,000 to ₹ 3,000,000 yearly.

The organization's size, industry, and location might influence NLP salaries. For instance, salaries for NLP engineers may be higher in tech companies like Google, Amazon, or Microsoft than in smaller startups. Additionally, those functioning in urban communities like Bangalore, Hyderabad, or Mumbai might procure more significant compensations than those functioning in other areas of the country. 

Similarly, in the US, salaries might be higher for individuals from metropolitan areas like Seattle, New York, or San Francisco than elsewhere.

Almost all industries employ NLP Engineers, but we will take a look at a few major ones below:

Technology 

Because this sector incorporates NLP into products and services, it is among the largest employers of NLP engineers. 

• Google, for instance, utilizes NLP in its voice assistant, translation tools, and search engine. Alexa voice assistant, product search, and recommendation systems are other Google features that employ NLP. 
• Amazon employs NLP for its Cortana voice assistant, Office productivity suite, and Bing search engine. 
• Apple's Siri voice assistant makes use of NLP.

Technology-based NLP engineers may be involved in developing new algorithms, creating new NLP-based products and services, and improving existing NLP algorithms. 

Finance 

This sector utilizes NLP to break down client information, recognize extortion, and develop client support. NLP can be used by banks, for instance, to look for fraudulent transactions or improve products and services by analyzing customer feedback and sentiment. 

NLP can be used by insurance companies to look for fraudulent activity and analyze customer claims. Financial NLP engineers may be involved in creating fraud detection systems, chatbots or virtual assistants for customer service, and algorithms and models for analyzing customer data. 

Healthcare 

NLP is used to improve patient outcomes, identify diseases, and analyze medical records in the healthcare sector. It can be utilized in the analysis of patient notes to identify potential health risks and in the analysis of research papers and clinical trials to identify novel therapies or treatments. 

NLP engineers working in medical care might be associated with creating calculations and models for examining clinical records and exploration papers, building chatbots and remote helpers for patient commitment, and creating illness discovery and finding frameworks. 

E-Commerce 

NLP is used by e-commerce businesses to improve customer service, product recommendations, and product search. NLP, for instance, can be used to build chatbots and virtual assistants for customer service, analyze customer reviews and feedback, and improve product recommendations. 

E-commerce NLP engineers may be involved in creating chatbots and virtual assistants for customer service, algorithms and models for analyzing customer data, and product recommendation systems.

Marketing 

Marketing firms use NLP to improve customer engagement, create content, and analyze customer sentiment. NLP, for instance, can be used to build chatbots and virtual assistants for customer engagement and analyze data from social media and customer reviews to discover trends and sentiments. NLP engineers working in showcasing might be associated with creating calculations and models for dissecting client information, building chatbots and remote helpers for client commitment, and creating content age frameworks. 

Online Media 

Social media companies use NLP to look at user data, find fake news, and improve the user experience. For instance, NLP can identify fake news and misinformation by analyzing user comments and sentiments. Algorithms and models for analyzing user data, chatbots and virtual assistants for customer engagement, and systems for detecting fake news and misinformation may all be developed by NLP engineers working in social media. 

Education, the legal system, and the government are among the other sectors looking to hire NLP engineers. NLP can, for instance, be utilized by educational establishments to analyze student feedback and enhance student outcomes, as well as by law firms to analyze legal documents and locate pertinent case law. NLP can be used by government agencies to get public feedback and understand their sentiment. As well as to develop policy documents and find potential areas for improvement. 

According to a recent report by LinkedIn, NLP engineering is one of the fastest-growing job roles in the tech industry. The report identified the top 15 emerging jobs in the United States based on job growth, salary, and demand, and NLP engineering ranked as the 5th fastest-growing job. 

Moreover, a recent study by Allied Market Research projects that the global NLP market size will reach $341.5 billion by 2030, growing at a CAGR of 40.9% from 2021 to 2030. This indicates a significant demand for NLP engineers across the globe in the coming years. 

In addition, a quick search on significant job portals such as Indeed, Glassdoor, and LinkedIn shows a high demand for NLP engineers in various countries, including the United States, Canada, the United Kingdom, India, and Australia. These job postings come from various industries, such as technology, healthcare, finance, and e-commerce.

We hope you found this article helpful in understanding the essential skills you need to succeed in this exciting and growing field. With the increasing demand for NLP applications and systems, there has never been a better time to pursue a career in this field. 

Remember, the key to success is to keep learning and honing your skills, whether it is by doing courses, participating in online communities, or contributing to open-source projects. By doing so, you can stay up-to-date with the latest trends and developments in NLP and stand out from the competition in the job market.