Tech Careers Part 2

Published on June 22, 2022

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Tech Careers: Part 2

By Hana Gabrielle Bidon

Tech careers offer people opportunities to work in various industries, such as business, finance, healthcare, and much more. Additionally, careers in tech can be pursued in several work environments from large tech corporations like Facebook and Google and consulting firms, such as Accenture and Deloitte. In the first article of the series about different careers in technology, I discussed some of the most popular tech career paths, including data science, product management, and software engineering. However, there are more careers that I left out in the first article. This article, which is the second part in this series, covers three more popular tech careers and tech-adjacent careers.

In this article, I am discussing three more tech careers and tech-adjacent careers:

  1. Machine Learning Engineering
  2. Technology Consulting
  3. Technical Writing

Machine Learning Engineering

Machine learning engineers utilize their knowledge in machine learning and software engineering with a focus on engineering. They deploy machine learning models to production and automate processes of analyzing data. Depending on the project(s), company, and industry, the responsibilities of a machine learning engineer varies.

Responsibilities of a Machine Learning

  1. Develop machine learning applications fulfilling business needs.
    • Machine learning engineers collaborate with front-end and back-end engineers to develop AI applications. They also communicate with product managers to understand business goals and how to meet them with machine learning.
  2. Analyze and improve machine learning algorithms.
    • To achieve these goals, they need to:
      • Understand business objectives
      • Develop, train, and select the best models that will solve business problems.
      • Retrain models to attain better results.
  3. Create business recommendations.
    • Use their findings and observations to optimize and improve the production of machine learning algorithms and display quantitative information into data visualizations.

To succeed as a machine learning engineer, here are some skills you need:

  • A solid foundation in statistics and probability, and mathematics (e.g., calculus, linear algebra, etc.)
  • Proficient in programming languages, such as Python, Java, R, and Prolog
  • Knowledgeable about machine learning frameworks and libraries, such as Keras, PyTorch, and Tensorflow
  • Good communication skills
  • Great problem solving skills

Technology Consulting

Technology consultants advise businesses on how to best utilize technology to their organization. To achieve this goal, they help business clients mitigate risks, streamline processes, and accelerate growth. In addition, tech consultants use the design and development or implementation of new technology. Different names of tech consultants include IT consultants, software consultants, computer consultants, and more depending on the speciality of the consultant.

Responsibilities of a tech consultant include some of the following:

  1. Working with business executives like leaders and managers to solve business problems, such as security risks.
  2. Advising the strategic and financial aspects of tech consulting. An example is how to invest in cost-efficient systems.

Some areas of tech consulting include security consulting, risk assessment analysis, IT infrastructure planning, and software development.

To become a tech consultant, it is ideal to have these skills:

  • Technical skills
  • Management skills
  • Business skills
  • Communication skills

Technical Writer

Technical writers create documentation communicating information or showing people how to do things. It is a broad field, which enables technical writers to various fields of science, technology, engineering, mathematics, and business.

Types of documents technical writers usually come in the form of business plans, technical specifications, policies, and more. Additionally, technical writers write documents showing people how to do things like training material, user guides, and other documents.

Most technical writing falls under two categories: end-user documentation or technical documentation for software developers, engineers, and more technical people. Examples of end-user documentation are a user guide you get with your smartphone and a Medium article showing how to publish an article to a publication. Some examples of engineering-related documents are guides showing technical people how to design software, cars, and electronic devices.

Technical writing follows a development cycle that is extremely similar to the product development lifecycle:

  1. Identify needs, audience(s), and scope
  2. Plan
  3. Research and content development
  4. Test or review and revision
  5. Delivery or production
  6. Evaluation and feedback
  7. Disposition (revision, destruction, or archiving)

Here are some skills to thrive as a technical writer:

  • Ability to write clearly and concisely in a neutral tone
  • Advanced Microsoft Word skills
  • Analytical
  • Curiousity
  • Great verbal communication
  • Excellent organizational and time management skills
  • Work well under pressure
  • Comfortably adapt to constantly changing conditions

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