I have been working at Zilliant full-time (More than 5 years)
The pricing science role at Zilliant is a great combination of customer facing and data science work. We're problem solvers first, and have to be comfortable using different tools and techniques (Tableau, SQL, Python, CPLEX, etc…) to build robust solutions for our customers. The role also has a strong customer facing aspect: we have to present our solutions to customers and help guide them through our models. This can be pretty challenging but we get great support from the science leaders who are very hands on and often get involved in projects, helping us with data discovery, solution design and presentation content.
Zilliant takes a lot of pride in promoting an adult culture across the company. Employees are treated with respect and are trusted to do their job well. We give regular feedback to each other to improve the way we work and we make a conscious effort to develop methodologies that could help all customer facing teams. This allows us to work really well across teams.
Last but not least, Zilliant is a true meritocracy where people who do well are recognized and get promoted from within.
Zilliant being a small company, the workload can be unpredictable so it's important that scientists have the capabilities to move quickly between projects. It is also important to take initiative as there's little hand-holding (this can be a downside of the adult culture to some people).
Benefits are not great. We only get 15 days vacation per year and there's no 401k matching.
Advice to Management
how can we scale faster?
I applied through college or university. The process took 2 weeks. I interviewed at Zilliant in February 2017.
At the start of the interview, they began asking questions about the resume that has been submitted. Those questions were very personalized and were based on the resume submitted. Not very many but just getting a feel for the interviewer. Then they have a technical question. The technical question was a database question with several parts: writing a schema, then questions about the schema that you write.