Natural Language Processing (NLP) is one of the fastest-growing areas within AI with its market set to reach $61 billion by 2027.
From faster analysis of medical records to predicting market trends through sentiment analysis, NLP can provide tremendous value to businesses.
This article provides insights and strategies for hiring NLP developers who are an integral part of developing these systems.
What Is NLP?
NLP is a subset of AI that enables understanding of human language by creating a system that interprets text and can automate various tasks. This field is a combination of machine learning, computational linguistics, and deep learning models.
What Does an NLP Developer Do? Roles & Responsibilities
NLP Developers design and develop systems that understand, interpret, and generate human language by using various text representation techniques. Some of the key responsibilities of an NLP Developer include:
- Understanding requirements for designing and developing NLP systems
- Pre-processing the texts and ensuring high quality of the training models
- Running experiments to test the effectiveness of NLP models
- Using text representation techniques such as word embeddings to create useful features
- Conducting statistical analysis of results and refining them
- Working with cross-functional teams to integrate the NLP systems
- Communicating to non-technical team members on what is NLP and its use cases
NLP Use Cases: What Is NLP Used For?
An NLP solution can save time of human involvement for 40% of working hours through large language models. This shows how the use of NLP can be lucrative for businesses.
Some of the most popular NLP use cases are highlighted below.
Before there were content editing tools, writers had to go through their content multiple times to spot any grammatical mistakes and edit them. Companies such as Grammarly have used NLP to detect the main points, help fix any grammatical errors, and suggest any improvements.
2. Conversational AI
Conversational AI has powered chatbots that provide a human-like conversation by recognizing the search intent of the user queries.
For instance, a chatbot called Endurance has been created as a companion for Dementia patients. It helps to identify potential degradation of memory function which can give an idea of the patient’s condition.
3. Voice Recognition
Another area where NLP can be used is with Voice Recognition where algorithms are trained to find the probable words based on the user’s search.
Smartphone features we interact with every day such as Siri and Google Assistant are based on this innovation.
4. Language models
Language models generate text and speech that seem as if a human has written them. The most popular language model GPT which is developed by OpenAI has been trained on a large dataset and mimics human intelligence.
5. Automatic Text Summarization
The processing of long texts into a synopsis of key messages is another area where NLP can be used.
By summarizing key documents, knowledge workers can save time on information gathering and fasten decision making. It has been predicted that such a use case can help to automate the tasks of even decision-makers.
Types of NLP
1. Named Entity Recognition
Name Entity Recognition(NER) helps with information extraction by identifying key entities within a text. NER is used in the healthcare industry to extract key information such as patient names, conditions, and treatments.
2. Sentiment Analysis
Among the different types of NLP, sentiment analysis is very popular as it helps to classify emotions from the text which is helpful for applications such as analyzing large numbers of customer reviews fast and with accuracy.
3. Text Summarization
This type of NLP helps to summarize vast amounts of text and extracts the key points to create summaries. It has many applications such as saving time for lawyers to get key points from a large amount of legal documents.
4. Topic Modeling
Topic modeling helps classify topics from texts without the need of human supervision. For instance, companies can use topic modeling to identify the key topics that their followers are discussing regarding their social media profiles.
5. Machine Translation
The use of machine translation helps to translate text or speech from one language to another. This is the same type of NLP that Google Translate has been using to serve more than 1 billion users.
What Are the Benefits of NLP in Business
To make the most out of NLP, it’s necessary to understand how exactly it can help a business. Here are the key benefits of NLP.
1. Efficient customer service
The high interactivity and continuous availability of chatbots created with NLP are able to handle up to 70% of complicated customer service queries.
As a result, businesses are able to serve their customers more efficiently along with accuracy in their responses.
2. Save time and money
NLP helps to save both time and money by automating tasks and accelerating the speed of existing processes.
A case in point, trading teams have been using NLP to read tens of thousands of pages of unstructured data in annual reports and security filings to get insights into a company much faster.
3. Boost your conversion rate
Additionally, NLP benefits businesses by increasing the conversion rate since there are multiple NLP solutions for optimizing websites and applications. This is done by creating personalized messages based on the user’s behavior.
4. Improve the end-user experience
The advanced functionalities that NLP presents can help to improve the entire experience for the user. For instance, social media platforms use NLP to automatically detect and remove any spam content.
5. Increase employee satisfaction
Using NLP can help to streamline unfulfilling employee tasks including scanning documents, data entry, and appointment scheduling.
With NLP, 45% of research analysts estimate that their roles will be significantly different in the coming 5 to 10 years
Key Skills to Look Out for When Hiring NLP Developers
There are essential skills NLP Developers need to have to produce great results. Below are some of the most important ones.
1. Technical Skills
Recruiters need to first prioritize assessing the technical skills since this lays the foundation for the work that an NLP Developer does. Some important technical skills include:
- Programming Languages: NLP Engineers need high proficiency in Python, Java, and C++. Furthermore, it’s essential to know the workings of big data frameworks such as Hadoop and Spark.
- Statistical Analysis: A candidate with strong statistical analysis skills will be able to work better with linguistic data and use the right methods for model training.
- Machine Learning (ML) Methods: During the technical interview, ask the Engineer about their experience with Supervised/Unsupervised learning, Recurrent Neural Networks (RNNs), and Transformer Models.
- Text Representation Techniques: Additionally, ask them for their know-how of text representation techniques like Term Frequency-Inverse Document Frequency (TF-IDF), word vectors, and paragraph vectors.
- Robust and Testable Code: Examine their portfolio and ask for their code samples to see if they produce clean and testable code.
- ML Frameworks and Libraries: Understanding of deep learning frameworks such as TensorFlow and PyTorch along with proficiency in Python’s Natural Language Toolkit( NLTK) are essential for NLP Developers.
2. Communication Skills
The ability to communicate complex concepts in a relatable way is an underrated soft skill that businesses need to prioritize. In fact, verbal communication skills are considered one of the key requirements in any job posting.
The complicated nature of developing an NLP solution requires facing numerous issues on a daily basis. A proactive candidate who can find the root causes of the problems and solve them helps these projects move forward.
4. Team Collaboration
Any NLP project includes working across a cross-functional team where empathy, a helpful attitude, and humility are crucial. Even a single team member who lacks these qualities may hamper the entire team’s morale.
There are constantly new changes and developments in this field which requires NLP Developers to adapt as per the situation. Consider asking candidates how they dealt with difficult situations in the past and came up with prompt solutions.
Common Mistakes to Avoid When Hiring NLP Developers
Recruiters come across various challenges during the hiring process that can lead to the wrong hire. Here are some common pitfalls to avoid before hiring NLP Developers:
- Neglecting Domain Expertise: It’s important to look beyond general programming skills and prioritize candidates with expertise in NLP
- Overlooking Technical Skills: Not assessing specific skills such as NLP frameworks and libraries may lead to an ineffective hire
- Ignoring Portfolio and Experience: Looking at the educational qualifications alone may not adequately measure the competency of the talent
- Neglecting Soft Skills: Even though an NLP Developer may be technically competent if they are hard to work with, it can lead to conflicts within a team
- Rushing the Hiring Process: By trying to fasten the recruitment process, important steps might be missed such as assessing a cultural fit and evaluating the candidate’s long-term compatibility
How to Hire an NLP Developer: Choosing the Perfect Candidate
Making a hire efficiently is great as long as the right hire is being made. Here are some tips for choosing the perfect NLP Developer:
- Define the Needs of the Company: Before starting the hiring process, it is essential to evaluate what the specific needs of the business actually are. This can include defining the duration of the role and the overall goal of the team the NLP Developer will work within.
- Create a Job Description: Be detailed in preparing a job description that aligns with how the role of the NLP Developer will actually look like
- Determine the Salary Range: Another important consideration is to understand the current market rates and finalize a realistic salary range that can land quality talent
- Choose Between Local and Remote Talent: Consider the pros and cons of going with either local or remote talent. Then, look into the relevant organizations or platforms where these talents can be found.
- Define the Key Skills and Qualifications Required: Defining the necessary skills and qualifications will help to filter the right candidates. For example, if the project is focused on Text Summarization then prioritize skills and experience on this first.
How Much Does It Cost to Hire an NLP Developer?
The average of an NLP Developer in the U.S. is $152,000 while in Canada it is $99,000.
The costs are significantly lower in Poland at $48,000 per year and Ukraine at $36,000 per year.
Best Models for Hiring NLP Developers
Based on the particular requirements to hire NLP Developers, there are three major models for hiring these talents.
In-house NLP Developers work as full-time staff who are paid a monthly salary.
- Control over Project’s Development: As NLP projects are complex and involve multiple parties, having an in-house team member can lead to smoother workflow
- Better Communication: These talents work as an integrated part of the team so it’s easier to collaborate with other team members
- Higher costs: The wrong hire can lead to an increased financial burden for the company
- Longer Time to Hire: The recruitment process can span from weeks to months
Freelancers are independent professionals working on a project basis.
- Lower Costs: Freelancers work on a project-by-project basis which helps alleviate extra expenses
- Immediate Availability: Unlike full-time hires who may need to complete a notice period, freelancers can immediately start work on the project
- Inconsistent Communication: Many freelancers work with multiple clients which might hamper coordination as per expectations
- Data Privacy Concerns: NLP projects require working with sensitive data which may be more risky when working with a freelancer
Outsourcing is about working with a third-party firm to delegate the hiring process.
- Quick Time to Hire: Their expertise and large network helps to find quality talents faster
- Businesses can focus on higher-value tasks: By delegating the recruitment process, companies can direct their energies on strategic objectives
- Mismatched Talent Expectations: Providing generic requirements may lead to wrong talents being recommended
- Coordination issues: There may be challenges with communication among the two teams
Hire Offshore NLP Developers
Remote work now accounts for 15% of all high-paying jobs. With more companies embracing a remote working environment, it has led to growing opportunities for hiring Offshore talents.
With clear benefits such as lower costs along with access to specialized skills, bringing in Offshore NLP Developers can provide a competitive edge
Why Hire NLP Developers from Poland/Ukraine?
Poland and Ukraine have grown as prominent names for offshore talents. Some of the reasons for this include:
- Strong Technical Education: Both of these countries have high-quality universities focused on technical education that produces quality tech talent every year
- Cost-Effective Talent: The lower costs in comparison to North America and Western Europe provide a higher return on investment
- Fluency in English: NLP Developers from these countries are proficient in English which helps for seamless collaboration
- Rich Pool of IT Talent: Polish and Ukrainian Developers are working on some of the most advanced NLP projects which has created a rich tech ecosystem.
- Cultural Compatibility: There is a similarity to Western standards in terms of work ethic and meeting deadlines.
Let RemoDevs Help You Hire NLP Developers
RemoDevs can help you hire NLP Developers with our database of expert candidates based out of Poland and Ukraine. We remove the hassle from the hiring process so you can focus on core business activities.
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