NLP Roles

Careers in Machine Learning (ML)

The demand for machine learning (ML) skills is growing rapidly. The ML roles similar to NLP. The technical skills with the highest demand for working in ML and NLP are:
  Python programming
  SQL
  Data science
  Data mining
  Data analysis

The following are some example positions involved with NLP applications:

Data scientist or data analyst

As a data scientist or data analyst, you use analytic and statistical skills to collect, analyze, and interpret large datasets. When you work on NLP applications, you might be asked to evaluate ML models to optimize outputs. For example, you could be asked to determine whether a topic modeling application correctly classifies topics, and whether classification errors are statistically significant. Data scientists and analysts often work with what are known as big data. Big data datasets are characterized by what is known as the four V’s (volume, variety, velocity, and veracity).

Data scientists and analysts acquire and prepare data. They also explore and analyze datasets to help select appropriate algorithms and models. When data scientists and analysts work with big data datasets, they develop strategies for handling the four V’s. Data scientists and analysts often validate findings and present recommendations to stakeholders.

Typical tasks: Acquire and prepare data, exploratory data analysis, present data analysis

ML Engineer

NLP engineers need some skills that are similar to a data scientist’s skills. However, as an engineer, you focus more on programming skills and software architecture. As an ML engineer, you apply those skills to designing and developing ML systems. An ML engineer who works on an NLP application would be more involved in designing and implementing the end-to-end solution for the NLP problem.

NLP engineers evaluate and select algorithms and models that are appropriate to the specific NLP business problem. They also design end-to-end solutions and select appropriate tools to create those solutions.

Typical tasks: Evaluating and selecting algorithms, training and evaluating models, designing NLP applications

Developer

Many software developers are now adding NLP functionality to their applications. For example, voice interfaces and text analysis that are based on ML are becoming commonplace. As an ML developer who focuses on NLP, your primary focus is software development skills, but you also need some of the skills of a data scientist and ML engineer.

As a software developer who works with NLP solutions, you are more likely to develop code that interfaces directly with an ML service (such as Amazon Comprehend). NLP developers often rely on either a data scientist or an ML engineer for their expertise with identifying and optimizing appropriate data sources and models.

Typical tasks: Incorporating NLP models or services into an application

Applied science researcher

You might also decide to work toward a career in science where you can apply ML technology to an NLP application. ML has had an impact on everything from astronomy to zoology, so many different paths are open to you. As an applied science researcher, your primary focus is on the type of science that you decide to concentrate on. For example, an applied science engineer might use ML to develop an NLP application that processes a large volume of medical diagnoses to look for patterns of misdiagnosis.

Applied science researchers can be from many different disciplines. The tasks they undertake are specific to their particular domain of expertise. For example, a biology researcher might use NLP tools to analyze findings from a collection of lab summaries.

Typical tasks: Using ML services to conduct research, applying NLP tools to a domain-specific application