Tech Hiring in 2023: Trends, Predictions & Strategies for Success | Datapeople's Maryam Jahanshahi

**Maryam Jahanshahi** (0:00)
The US. I think spends $17 billion on executive search every year.
And it's not just like senior level roles where that's happening. We see that's happened in junior level software engineer roles. And I think what I think is a really exciting trend is unfortunately caused by the economic situation. We need to, like people need to be able to talk about efficiencies in the process.
And that's where their focus is. And so that's causing recruiting to sort of be like, what are the costs of all of our channels and which ones are actually doing well for us? It's becoming a lot more data driven than it's been in the past.

**Conor Bronsdon** (0:36)
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We are back on Dev Interrupted. I'm your host, Conor Bronsdon, and we're live from New York with another incredible guest. Welcome to the show, Maryam Jahanshahi.

**Maryam Jahanshahi** (1:24)
Thanks, Conor. I'm excited to be here.

**Conor Bronsdon** (1:26)
And I really love that you're here because we don't talk to data scientists that much, and you are not only the head of R&D and a data scientist at Datapeople, you are also a co-founder of that company.

**Maryam Jahanshahi** (1:37)
I am a co-founder. I also work very strongly with engineers, and so I'm always up in their code and in their pull requests and all the fun side of things. I guess that's one of the things when you get to be a technical co-founder, you sort of have to run the gamut of all the different things that you do.

**Conor Bronsdon** (1:53)
Yeah, it was fun talking to you as we were kind of getting set up, and you mentioned you kind of had this opposite journey where you really dove into the data side, they were becoming like this strong data scientists, and then you realized you wanted to add these data engineering skills to the table.

**Maryam Jahanshahi** (2:05)
Yeah, it's an unusual experience. I think part of the reason why I had to do it was I had to figure out the systems that we needed to analyze data to kind of get a data-driven product. And so my role now is sort of, it is such a weird mishmash. I was talking to my co-founder about it the other day. I'm neither like, nor do I run engineering, nor do I run like the data side of things, but it's sort of almost technical product management.

**Conor Bronsdon** (2:32)
You're the fusion between the two of them.

**Maryam Jahanshahi** (2:33)
It's like a weird mix of many different things. And so we're realizing that, you know, that requires a certain level of skills and different types of agility. And so it was easier for me to actually write my data pipeline than write the spec to give it to the engineers to do. So I was like, yeah, this isn't so bad. So we realized very early on, like with these systems, you, I think increasingly as the tools, as our data becomes bigger, we're going to have new classes of product managers, including like data-informed product management. I'm sure things like ChatGPT are bringing that to the fore, but it's not just that. It's anything that adds a level of analytics to your dashboards and things like that. Like you want someone who has a business interest, but also is able to like run the SQL query to figure out what the hell went wrong with that dashboard. And so it's an interesting transition, but I don't know whether I'm crazy for making it, but it's what the organization needs, so it ends up being a fun place.

**Conor Bronsdon** (3:30)
I think it makes a lot of sense, right? Like if you're a data scientist today, you want to add that skillset so you can better interface with these tools that are able to extend and leverage your work.

**Maryam Jahanshahi** (3:40)
Correct. And it's been really like, so I taught myself, Hythin, mostly to sort of get a sense of how do we build very reproducible processes to understand data, and then that's been a gateway drug into natural language processing, which obviously at Datapeople, we take career documents, whether it's resumes or job descriptions, and sort of take and extract really important structured data so we can run analyses and understand the impact of things. So that's been my kind of gateway drug into things, but I sometimes read a little bit of PHP because that's what our lovely monolith is written in. And so it ends up being, once you know one of them, you start getting a sense of the others.