Database Reference
In-Depth Information
Five years from now, we will have enough understanding of how companies
do business to give them more insights across an entire vertical. We want
to be able to tell them: “This is the golden range of where you should oper-
ate. This is what we've seen companies do in the past, and this is what you
should do.”
Gutierrez:
What is the makeup of the SalesPredict team?
Radinsky:
We're a company whose engineering is in Israel and whose sales
and marketing team is in San Francisco. This means that I, as the CTO, am in
Israel and our CEO is in San Francisco. The CEO and I are the co-founders
and we previously worked together at Microsoft. This shared history enables
us to work together closely, even though we're in different parts of the world,
because we've been working together so many years that we're at the point
we each know what the other one is thinking.
The reason our engineering team is here in Israel is that the engineering talent
here is amazing. There is a bond between many people that help to achieve
higher goals—we grow up together and we go to the army together. The
army is the most amazing incubator for technological people. So we have a
really fruitful environment here. However, I believe sales and marketing should
always be where your target market is. Currently, our target market is the
United States, so that's where the sales team for SalesPredict resides.
We're a startup, so we're only fifteen people. Of the fifteen people, we have
ten people on the engineering team. Everyone is a data scientist and engineer
in the engineering team. Of course, though everyone is a data scientist, some
come with a more theoretical background, some come with a more data
science-centric background, and some come with more hard-core engineer-
ing backgrounds, where they have scaled entire systems before.
I enjoy both aspects—the engineering and the data science. My passion, of
course, definitely has to do with data. However, this work is not worth any-
thing if I don't actually make it work, so I really like the engineering aspects
of implementation and seeing that happen. This is why I think a data scientist
should be, first and foremost, a great engineer. My role is about thinking how
can we take this technology and move it forward more and more. So I work
with the algorithms on the business side of the application, define how we're
going to make it all work, and then work together with engineering. We're a
small team, so I try to do less managing and more working.
Gutierrez:
How does SalesPredict work?
Radinsky:
We are a cloud-based solution that, after authorization, connects
to our customers' CRM (e.g. SalesForce) to access their sales data. Our algo-
rithms look at where the company managed to sell to in the past and who
are the people that managed to make those sales. We collect all the data they
have about those people from their CRM. When I say we collect all the data,
