Putting a Face to the Framework: Katerina Hristeva

Hyperscience | Posted on September 9, 2021

With the recent launch of our enhanced Hyperscience Platform, the cutting-edge power of our ML-based extraction again takes center stage. Much of the hard work in developing solutions happens behind the scenes, thanks to our diligent QA Engineers. The entire QA team works in parallel to ensure all aspects of our products are sufficiently covered by automated tests.

Collaboration and innovation is the heart and soul of the team, says Katerina Hristeva, who joined Hyperscience two years ago. We recently chatted with Katerina about how our warm culture and top-notch technical talent have helped her develop personally and professionally.


QA Engineer Katerina Hristeva

What brought you to the field of Engineering? 

Prior to joining Hyperscience, I worked in real estate. Although I worked in an entirely different industry, a friend gave me a programming book, and I was fascinated by the process of writing and testing code. I was eager to learn more about these problem-solving concepts and how they simplified workflows. 

Talk to us about the QA Engineering team. What’s your favorite part about working with this team?

I’m always learning and collaborating with my team at various levels of the testing process. Everyone on the team is incredibly experienced, and we work together to ensure our solutions are integrated in a reliable and scalable way to bring real value to customers. 

I love working with my team to promote customer confidence. A positive user experience makes or breaks a product. By meticulously testing the product, users are much more satisfied. Exceeding customer expectations is a great feeling. 

How much of your day do you typically spend coding?

I spend about 70% of my day coding. I prefer to automate tests for the new features, which helps the team execute test cases multiple times at a scale that is not possible on a human level. I spend the other 30% of my day writing acceptance criteria, performing manual feature testing, logging bugs, and, of course, meetings. 

What’s the most rewarding part of your work at Hyperscience?

I’m thrilled about the quality of product we deliver. When I see the high quality product prior to the release, I’m really proud of my team for their hard work. Our collaboration interdependency as a team helps to optimize opportunities for success. 

What’s one piece of advice you have for aspiring women in tech? 

Remember, your career will be long. Your early working environment will have a long-term effect on your approach to work, your confidence and how you view technology overall. I encourage you to lean into training and mentorship opportunities that allow you to grow your skills and will put you on the path to a fulfilling career.

Learning does not end once you graduate from school; it’s a life-long process. We sometimes fail to recognize that our career is not the end product of our education. On the contrary, a thought-provoking career will continue to educate and inspire us.

What would you suggest is the ideal tool for emerging QA Engineers to utilize?

The Quality Assurance profession requires individuals to skillfully combine a variety of tools contingent upon the product being tested. 

The biggest technical skill to possess is a thorough understanding of the software they are working with. Up-and-coming QA engineers should familiarize themselves with a programming language. Candidates should then get familiar coding.  Especially as automation takes on a wider role in QA efforts, knowing how to code has become essential for anyone in software testing. 

Do you have any favorite productivity hacks? 

A good night’s sleep! I try to get eight to nine hours of sleep every night. That way, I’m more energized and alert the next day. 

What excites you most about the future of Hyperscience? 

We recently enhanced the Hyperscience Platform. This new version combines our ML-based extraction with data validation and enrichment capabilities. Organizations continue to be tasked with manually pulling  information from complex documents and disparate sources. I’m excited to see it transform the way our customers do business with easier integration, configurability, and accuracy. 

Interested in learning more about ML and AI but unsure where to start? Check out Hyperscience Learn an educational program developed by our team aimed at knowledge-sharing and community-building within the ML community. 

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