In an ideal working environment, your abilities would be the major factor determining the success of your career trajectory. However, systemic review of predictors for successful careers has shown that success is not defined by just your abilities. In addition to one’s professional competence, true success is more readily defined through other significant factors. Reasons such as satisfaction with your given subject, occupational stress and quality of life all matter.  And when these areas are unmet, or, if there is a barrier to obtaining these things, success becomes less likely an achievable goal.

Elizabeth Wu and Danika Khong experienced several of those barriers when attempting to build successful scientific careers. They themselves had been subject to what comes when bias begins creeping into scientific hiring and recruiting practices and connected through their shared desire of wanting to match the right work environments with a postdoc and graduate student’s training needs and skills. These women scientists have worked to reduce those barriers in academia, and to show how small interventions can significantly reduce the impact that bias have on one’s career. Through their shared vision to leave scientific research labs in a better state than that in which they found it; and, funding from the Digital Science’s Catalyst grant these #WomenInSTEM took their theoretical idea and developed a functioning tool. Scismic is a talent matching platform built to help researchers navigate the process of finding jobs that align with their exact skillsets.

Today, I interview Elizabeth and Danika about some of the ways Scismic has been used to help diversify research labs, how publishers in STEM might aid in their efforts to help scientists match jobs that fit their strengths and priorities; and, how they plan to use this tool to address the $1 billion annual loss from inefficiencies in scientific recruiting in the US before expanding around the world.

Job finder button for online job search

What was your shared experience with scientific hiring and career path progression, and how did it lead to the development of Scismic?

We started our careers in academia and eventually transitioned to positions in the biotech industry. As we faced challenges in our career transitions, we realized we were ill-prepared for this process, having mostly been trained to pursue only academic careers. Therefore, we started to build tools to help our fellow scientists think about their career goals in industry earlier in their training programs. We teamed up with one of our co-founders, Danny Gnaniah, a platform engineer and marketing expert, to make that happen. Hence, Scismic Job Seeker was born.

What is the purpose of Scismic and the Job Seeker tool?

Scismic was founded by a group of scientists who are passionate about fostering innovation in the sciences. We wanted to drive more therapies to patient bedsides, and make sure the health problems of ALL communities are addressed. We do this by building online tools that accelerate innovation. One of these platforms is Scismic Job Seeker. With Scismic Job Seeker, we prepare researchers for jobs in both industry and academia. Our technology also promotes inclusive hiring processes and matches scientists to jobs that empower them.

What do you see as part of the inefficiencies in scientific recruiting? And how can this tool be used to help?

When posting on most job boards, a recruiter/hiring manager can receive 200+ CVs for a single position. We found that companies spend up to 35 hours screening CVs for one position, and often overlook qualified candidates because of keyword misses. In addition, research shows that implicit biases often exclude qualified candidates in the traditional screening process. Collectively, hiring managers spend a lot of time and money finding someone with the required skills and drive.

Scismic Job Seeker eliminates this manual and time consuming process of screening CVs for the right skill sets. Our technology brings life science companies the scientists who are qualified and interested in a position. We believe that when a new hire immediately provides value to a team, the team is on the right path to productivity.

How exactly does Scismic work to match skills with recruiters? And what does the data show about its impact on diversity?

We uncovered a major disconnect between the language scientists use to describe their skill sets and the terminology recruiters use to describe skills they are looking for. So we built a sophisticated taxonomy that would translate the language of a scientist to the language of a recruiter. This means that scientists would be meaningfully connected to jobs that needed their skill sets. We knew our taxonomy was pretty effective when we observed that 50% of our applicants get called back by hiring managers.

So far, 60% of all scientists hired through Scismic Job Seeker are scientists who would normally face age, gender, or racial biases in hiring. We are currently testing new features to add into Scismic to further delay the introduction of potential biases in the hiring process.

How does Scismic help to increase racial diversity in STEM?

We believe that 2 key factors are essential for Scismic to help drive racial diversity:

  1. Having underrepresented scientists on our platform; and,
  2. Having a method to reduce the biases associated with candidate evaluation.

For Scismic, it is too early to have robust data sets, but we are working hard to achieve those two factors. We actively work with populations of underrepresented scientists. We were awarded a grant from the NIH to develop and test features that could delay the exposure of a candidate’s biographical information to the hiring manager. Currently, we have promising preliminary evidence: 60% of all scientists hired through Scismic Job Seeker are scientists from age, gender, and racial groups that would normally face biases in hiring.

What about other types of diversity, beyond race and gender? Can this tool be expanded to help infuse diversity in other scientific environments outside of the lab, or, other, non-research work environments?

Yes, one candidate was hired through Scismic who felt he had faced barriers in previous applications because he was older than most candidates for someone transitioning out of academia into industry. So Scismic also eliminate biases against age, universities, geographical regions, and other biographical information in the initial matching step.

We are already addressing roles off the bench, including communications, business development, and regulatory affairs.

Our technology is applicable to any field that requires specialized skill sets and knowledge. We can apply our existing system to other fields in the future.

What role does anonymity play in the process?

Scismic Job Seeker’s system shortlists candidates without taking into account any biographical information that may exclude certain candidates during the screening process. We are currently investigating whether delaying the presentation of biographical information in candidate evaluation will result in more underrepresented scientists being moved forward.

How might this tool be used in scientific publishing? For example, to help with reviewer recruitment, or, other areas.

We have not yet served the publishing industry, but the platform is ready to be used for this purpose. For example, if editors are seeking reviewers in specific fields of expertise, Scismic Job Seeker could be used to help connect the editor to qualified reviewers.

What advice would you give to publishers to help our researchers drive better hiring practices?

  • Examine your own team. Does your publishing team have an inclusive hiring process? Get demographic data on your team’s workforce and ask about efforts to ensure that everyone’s voices are heard in your organization.
  • Get your readers educated. Inform your readership of the importance of diversity, not just to be an ethical organization, but also to ensure their organizations cover as many blind spots as possible with different perspectives.

Has there been any negative feedback about the tool?

We have received unexpected pushback that our “diversity-promoting” efforts could put non-underrepresented scientists at a disadvantage in our system. However, since our platform removes biases to put everyone on equal footing in candidate evaluation, we are not giving any groups an unfair advantage.

How do researchers gain access to Scismic?

Scismic offers subscription packages to life science companies that are looking for scientific expertise. Scismic is completely free for scientific candidates and job seekers.

What’s next for your team?

With our NIH grant award, we are expanding our diversity-promoting features to remove more sources of bias from candidate evaluation through our system. We are currently testing the efficacy of these features.

In addition, Scismic recently joined Digital Science’s portfolio of companies to foster innovation around the world and enable every scientist to make their greatest impact.

Jasmine Wallace

Jasmine Wallace

Jasmine Wallace is the Senior Production Manager at the Public Library of Science (PLOS). She is responsible for the production processes and day to day production and publication operations for the PLOS journal portfolio. Previously, she was the Peer Review Manager at the American Society for Microbiology (ASM). She was responsible for ensuring peer review practices, workflow, processes, and policies were up-to-date and applied consistently across the entire portfolio of journals. She currently serves as Treasurer for the Council of Science Editors and is the creator and host of their podcast series S.P.E.A.K. In the past, she was a Teaching Assistant at George Washington University for a course on Editing for Books, Journals, and E-Products.


1 Thought on "Getting Beyond Bias to Make the Career Impact You Desire: An Interview with Scismic’s Elizabeth Wu and Danika Khong"

How does Seismic find these underrepresented scientist candidates? They haven’t stated, though they have state that employers cannot, for example, search for candidates who have attended HBCUs, are first gen, etc.

Given much of their tool is machine-based, how do they work to remove the bias from their source data and continuously improve their data to remove bias?

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