Update: Hiring at Arcadia

Back in January, we posted the below article on our hiring strategy. Since then, we’ve launched twelve searches and hired seven candidates, and in that time, we’ve learned quite a bit about our hiring needs and processes. 

The values laid out in the blog post still hold: we are dedicated to building a diverse (along multiple axes) team based on skillset and culture fit — not on inequitable metrics of success. 

But as we’ve grown, we’ve evolved our approach to better suit our needs. In the spirit of open science, here are our top four lessons and how we’ve adapted.

1. Hackathons are hard. The benefits of a (fully compensated) hackathon, as laid out below, are many, but so are the challenges. We were dedicating an immense amount of bandwidth to planning and coordination, and a global pandemic didn’t make this any easier. We decided to shift our approach to a skills-based assessment within the framework of a standard interview process for most roles. This still allows us to test candidates on their abilities and to unearth unexpected candidates, but without the complex superstructure of a hackathon. In particular, we’ve seen value in including screening questions at the initial application stage. The most effective questions are specific and practical, allowing the candidate to demonstrate their skills. This approach has given several qualified, mission-aligned candidates a significant boost. 
   
2. Passive recruitment doesn't cut it. To realize our workforce ambitions, we need to put in the work. We found that posting our job ads on our website and LinkedIn didn’t ensure a sufficiently deep and diverse candidate pool. We’re now incorporating an active scouting step in our recruitment, reaching out directly to the types of people we are hoping to attract. This active step involves searching LinkedIn, utilizing our advisors and their networks, scouring preprints, and more. While energy-intensive, this effort has paid off, bringing in excellent candidates and extending our own network.
   
3. One type of search doesn’t fit all. As we launched more job searches, we realized that they tend to fall within two distinct frameworks that require different workflows. To improve efficiency and to better align expectations with candidates, we now define these search types and their goals. This allows us to move quickly and precisely when needed, but also to be more open-ended and exploratory when appropriate. Both searches are important for building our dream team. 
   
   An “Active Search” is where we have a clearly-defined and specific need and are actively looking to fill a position in the next two to three months. For these roles, we intend to move relatively quickly once we find the right candidate. 
   
   An “Open Search,” on the other hand, is more flexible in terms of timing and skill set. We want to explore with a wide range of candidates the opportunity and scope of potential solutions. As such, this process is intentionally open-ended, and promising candidates should expect a dialogue about creative possibilities and mutual fit. Importantly, the job description may evolve as a result of these conversations and the timeline of these searches is highly variable. We do appreciate that this type of search was not the expectation of those who initially applied to the roles we have recategorized as “Open Searches” and may not be ideal for all job seekers, but we aim to be as transparent as possible on our shifting needs and associated timing.
   
   This bifurcation allows us to fill the key roles we know we need efficiently, as well as leave the door open to serendipity and surprising results.
   
4. Scarcity is a mindset, and we should break it. It is certainly true that there is a hiring surge in biotech — particularly in the Bay Area — right now. Candidates are getting multiple offers and companies have to be competitive. Even still, many candidates, particularly those coming straight out of academia, don’t truly appreciate their value. We encourage job seekers to explore, apply, and interview at other companies, in addition to Arcadia. We believe that an empowered candidate, who understands their full range of possibilities, will likely be a better fit than one who thinks this is their only option. 

Our hiring strategy is an ongoing experiment and we will continue to refine our processes as Arcadia grows and our needs change. Hiring remains hard, but we will continue to iterate as our team is — by far — our biggest asset. 

Posted January 18, 2022

Hiring is hard. Hiring at a new company that is trying to innovate in multiple dimensions and break free of traditional constraints is extra hard. But building our team is also the most essential and important activity for a company at our stage. We’ve got to find the people who can see the vision, who have the skills, who share our values, and who are ready to make big — sometimes scary big — steps forward. If we get hiring right, then everything else we try to do will be infinitely easier.

At Arcadia, we look at problems from first principles and question whether the inherited wisdom holds true when divorced from the confines of traditional institutions, like academia and biotech. In science, we’ve been so locked in to inequitable metrics of success (publication in specific journals, appointments at particular institutions, associations with certain labs, big name fellowships and awards, etc). However, these are not the right proxies for the type of scientist we need at Arcadia. We are actively challenging ourselves to ensure that we don’t rely on old metrics in our new system.

We also recognize the incredible, untapped talent of people who never found their fit in traditional scientific institutions. We have to find new ways to unearth unexpected candidates, to work with candidates to determine if there’s a skills and culture fit, and to capture the wealth of ideas and solutions contained in our candidate pool.

With these goals in mind, we are setting up a number of experiments in hiring based on demonstrable abilities through hackathons. Hackathons have long been used as a hiring and talent discovery mechanism in the tech industry, but are far rarer in the biological sciences. Our hypothesis is that by creating shared projects that candidates work on alongside Arcadia scientists, we will get a far more realistic view of a person’s skills and workplace demeanor than the traditional interview process. Hackathons will allow us to explore challenging questions — like how to use bioinformatics to identify the right organisms to answer specific biological questions — with large groups of scientists with varied backgrounds and interests, giving us a better shot at finding a truly useful solution. Lastly, we want our hackathons to benefit all the participants, and not just the person eventually hired, so we are working to structure them so that everyone gains skills, connections, and resources, and are fairly compensated for their work.

We’ve already completed one successful hackathon — the results of which you can check out here — to find a cohort of science communicators to help us articulate and share the awesome science at Arcadia and beyond. We deem this hackathon a success, not only because we are absolutely delighted by the finalists and their final products, but because through this process we found communicators with a ton of potential and skills, but that don’t necessarily have a long list of previous professional writing credits at big name media outlets. Thus, this process allowed us to look beyond what would be assumed to be the most important metric for hiring and to find great candidates that fit the criteria that is actually the most important to us: the ability to do the work well.

Of course, hackathons aren’t the answer to every recruitment need, and there are positions that aren’t amenable to this type of evaluation. We also acknowledge that devoting so much time to an interview process can be challenging for candidates, and to that end, we set up a survey to find out how we can make the hiring and hackathon process better for all parties.

Hiring is still hard, and this is only the first iteration on a possible solution, but what’s important is that we are running the experiment, testing our assumptions, and looking for new answers to old problems.