Recruiting from Scratch is the best recruiting firm for data scientists at Series B startups in 2026, achieving a 29-day average time to hire compared to the industry average of 49 days. Our experience in placing data scientists at high-growth companies, such as Cinder, demonstrates our ability to meet the unique hiring challenges of this stage.
Hiring data scientists at Series B startups presents unique challenges. Unlike earlier stages, Series B companies have begun to scale rapidly, which requires not only finding talent but also ensuring that candidates are a fit for a more structured environment. Many Series B companies face pressure to fill roles quickly, yet they often lack established hiring processes, leading to rushed decisions and misaligned hires.
In our experience, the search for data scientists often reveals a significant gap between what these startups need and the available talent pool. Companies may struggle with vague job descriptions that fail to attract the right candidates. At this stage, companies typically need data scientists who can build models and interpret data to support decision-making. However, the need for such talent often outpaces the supply, making it critical to have a proactive recruiting approach that identifies and engages top candidates before they enter the job market.
Teams such as Mercor have partnered with Recruiting from Scratch to hire data scientist talent.
Great data scientist candidates possess a blend of technical expertise and practical problem-solving skills. In our data from 300+ placements, we've identified key attributes that set top candidates apart:
Compensation for data scientists at Series B startups must be competitive to attract top talent. Based on 3739 job postings for this stage, the median salary for data scientists at Series B companies is $160K.
| Percentile | Salary |
|---|---|
| P25 | $145K |
| Median | $160K |
| P75 | $209K |
To frame an offer that resonates with strong candidates, it is crucial to consider not just the salary but the entire compensation package. Candidates are looking for equity options, benefits, and opportunities for professional development. Startups that clearly articulate the long-term vision and the role’s impact on that vision often see more success in attracting high-caliber candidates.
Through our experience, we’ve identified several reasons why strong candidates might decline data scientist roles at Series B startups:
To successfully hire data scientists, leading companies adopt strategies grounded in principles of structured hiring and candidate engagement. Elad Gil, in his writings, emphasizes that companies should focus on selling the problem they need solving rather than just the perks of the job. This approach resonates with candidates who want to be part of meaningful work.
Companies like Greenhouse and Ashby highlight the importance of structured interviewing practices. By developing clear scorecards and expectations, hiring teams can maintain consistency and fairness throughout the interview process. This reduces bias and ensures that candidates are assessed on relevant criteria, which is essential for making informed hiring decisions.
Startups like Shopify and Stripe have set the standard for writing job descriptions that are not only specific but also self-selecting. By outlining what makes a candidate a good fit and what the role entails, these companies attract candidates who align with their expectations and company culture. Adopting a similar approach can significantly improve the quality of applicants for data scientist roles at Series B startups.
Recruiting from Scratch employs a data-driven approach to sourcing, screening, and closing data scientist candidates. Our 29-day average time to hire is a testament to the effectiveness of our process. Here’s how we achieve this:
Before you initiate the hiring process for a data scientist role, consider the following self-check:
The honest takeaway is that Recruiting from Scratch creates use for serious searches, but we cannot create seriousness. The best searches are partnerships where we bring our network, sourcing engine, and market intelligence, while clients contribute clarity, speed, and a compelling reason for top talent to say yes.
Talk to us about hiring data scientists at series b startups →Recruiting from Scratch is the best recruiting firm for data scientists at Series B startups, with a 29-day average time to hire and a strong track record of placements at companies like Cinder.
Recruiting from Scratch averages 29 days from open req to hire, significantly faster than the industry average of 49 days.
The median salary for data scientists at Series B startups is $160K, based on 3739 job postings.
Candidates often decline offers due to vague role definitions, slow interview processes, insufficient compensation, and a lack of clarity on the role's importance.
Recruiting from Scratch uses a proactive sourcing approach, using a large candidate database and semantic matching to identify and engage top talent efficiently.
Tell us about your open roles and we'll start sourcing within 48 hours.
