Data science recruiters for high-growth tech companies

Our customers are venture capital-backed, high-growth tech companies, from startup to growth stage and enterprise; they range from 15 employees to over 50,000. Companies usually call us about data hiring when they close a funding round and have a hiring surge, make their first data or machine learning hire, have had data hires that didn’t work out, or need more recruiting capacity for technical roles without adding W-2 recruiting headcount.

We fill data scientists, data engineers, analytics engineers, data analysts, and product analysts, from associate through senior, staff, and principal levels, as well as business intelligence developers and analysts.

Data science sits inside our technical recruiting practice. We work as data science recruiters for high-growth tech companies at every stage, and the recruiter on your search has hired data talent at your stage before.

We also fill machine learning engineers, MLOps engineers, AI engineers, generative AI engineers, and AI research scientists.

These roles have changed quickly. Once large language models arrived, many machine learning roles were rebuilt around them for a while. They’re now settling into machine learning and prompt engineering as the main focus areas, and each company defines the job a little differently, so we scope every search on what your team is building.

What good looks like is shifting too. In our experience, reasoning, problem-solving, and system design are becoming the markers of a strong machine learning or AI engineer, along with how well they direct AI tools and understand what comes out of them. Your company’s own policy on AI use decides which secondary skills matter most.

Data teams divide the work differently from one company to the next. At one company, a data scientist runs analysis and experiments for the product team. At another, they build and ship the models inside the product. We scope the search on the work the role needs to do and the impact the person has had, whatever their last title said.

An important thing to consider is your company’s stage. A startup’s first data hire often has to build the foundations themselves, with no data engineers or platform team around them. Someone who has spent their career on a large company’s established data platform may never have set one up, and at your stage that’s the job.

Sometimes the reason for targeting a company is overlooked. Sourcing from a well-known company’s data team makes sense when you know how that team works and want yours to work the same way. When the only reason is the company’s name, the search can lose months to candidates who don’t fit what you’re building.

Another challenge is holding out for a perfect candidate. A team meets strong data scientists, runs several through the full process, and still doesn’t make an offer because nobody stood out enough. Months later, it realizes it could already have hired some of the people it interviewed.

We start with your hiring manager: what data the role works with, whether the person will analyze it, build pipelines for it, or build models on it, what’s already in place, and who they work with day-to-day. Then we go past the job description: the backgrounds that haven’t worked out before, the red flags, and the questions that tell a recruiter in a first screen whether someone is worth your team’s time.

For data science and machine learning roles, we recommend holding the technical assessment until after the hiring manager interview, once the candidate has heard about the problems your team is working on. Asking for hours of work earlier leads more strong candidates to drop out. We recommend a take-home the candidate can use AI on, followed by a live review with one of your data scientists or engineers where they walk through their approach and build on it.

We also check track records. We tell candidates in the first round that we require references from previous direct managers at the offer stage. How candidates react to that tells us a lot.

At kickoff, we review the roles with your hiring team and align on the interview process. Within 48 hours after the kickoff meeting, we meet with the hiring managers again to have a calibration meeting. We bring 10 candidate profiles to the table to ensure that we’re aligned and only interviewing relevant talent.

1. 48hours

   Calibration meeting

2. 3-7days

   Initial recruiter screens

3. 7-10days

   Hiring manager interviews

4. 2-3weeks

   Full qualified candidate pipeline per role

5. 30days

   Initial hires

6. 90days

   On track against the hiring plan

Initial recruiter screens happen in 3-7 days, and hiring manager interviews in 7-10 days. You have a full qualified candidate pipeline per role in 2-3 weeks. Initial hires typically land in roughly 30 days, and you’re on track against the hiring plan by 90. Time-to-fill runs 15-60 days depending on seniority and scope.

A common hiring mistake is picking the best of the first handful of people you speak to. We focus heavily on outbound sourcing to engage with a lot of talent. This ensures that you’re able to hire the best people on the market.

Along the way, our recruiters meet with your hiring managers weekly and communicate updates daily in your chat channels. You also get weekly metrics from the delivery lead who manages recruiter performance. Our recruiters also have access to our tech stack that accelerates identifying and reaching out to top talent: strategic AI sourcing and AI resume matching, multi-step, multi-channel outreach, and job postings. Everything runs in your systems, and you own your data and your candidate pool.

Most of the data science and machine learning hiring we do runs through Embedded Recruiting and RPO: a recruiter working only on your business, inside your systems. If your hiring is consistent, or there’s enough work for at least one full-time recruiter, it also cuts down on cost-per-hire. The engagement scales with your hiring plan, from adding capacity to your internal recruiting team to running recruiting as your talent function. You can see how these engagements run in our case studies.