Who’s in the Genome Money?

By Adrienne J. Burke

Are you paid what you’re worth? Would you be better off in a bigger company? At a government agency? With a more advanced degree? The charts, graphs, and stats on the following five pages break down the results of Genome Technology’s salary survey every which way we could think of slicing them.

We tell you who gets the best pay by job title, company size, and sector (public or private). We tell you which organization types have had layoffs, where in the country the most paycuts have happened, and which employees seem most confident of raises and bonuses this year. Whether you’re a CEO, senior scientist, or core lab manager, you should be able to measure your position against your peers.

One caveat: This being our first annual salary survey, what we can’t tell you is how the current career climate compares to years past. More than one-fourth of respondents went without a pay raise in the past 12 months, and an even greater number expect no raise this year. While only four percent of respondents had the personal experience of being laid off in the past year, nearly half of them witnessed their colleagues get the pink slip. Is this better or worse than years past? We’ll have to wait till 2004 to do that kind of reporting.

But we can tell you how you compare to the general population. Although the Bureau of Labor Statistics does not collect data specific to genomics sector work, the mean annual salary for the group it calls life, physical, and social science occupations was $49,710 in 2001. Among GT survey respondents, the most commonly reported salary range was $75,000 to $99,999.

We can also tell you that as a group, GT readers have come a long way. We asked respondents to tell us what their salary range was five years ago. On average, you say you’re earning about $50,000 more today than you were five years ago. That means you’re doing far better than cost-of-living increases each year, but we have a feeling this group has been climbing the ladder a little faster than your average professional.

Methods and Respondents

Before you get to the numbers, here’s a little about our methodology and who responded. In early June, we sent e-mail notices to 11,890 GT subscribers, inviting them to click on a Web link that would take them to our 20-question survey. As an incentive, we offered respondents the chance to enter a drawing to win a year’s worth of access to one of our weekly paid-subscription newsletters. The survey was open for approximately one week, and 1,521 people responded in that time.

Of those, 38 percent say they work for a pharmaceutical or biotechnology company. Another 28 percent work at universities, and nine percent are in government. The remaining 25 percent are dispersed evenly over agriscience companies, computing companies, analytical instrumentation companies, software or datamining tools vendors, providers of consumables or lab supplies, life sciences consultancies, and other public-sector organizations.

The largest group, 23 percent, works in midsize organizations of between 500 and 5,000 employees. Another 22 percent are employed by organizations of more than 10,000 employees. Nineteen percent work for organizations with between 100 and 499 employees, and the rest work either at small organizations of 100 or fewer employees, or at an organization with between 5,000 and 10,000 employees.

Most respondents — 39 percent — list their job title as either senior level or staff level scientist, researcher, technologist, or programmer. Another 18 percent are at the senior level with the titles chairman, CEO, president, COO, CSO, CTO, vice president, director, or senior manager. The titles of the remainder include lab technician, service engineer, business development, professor, principal investigator, post-doc, core lab manager, technician, and technology transfer officer.

Our respondents are a highly educated group. More than half hold PhDs. Three percent have MDs, and six percent have MBAs.

Asked to name the single scientific or technical task that occupies the largest portion of their workday, 22 percent say gene expression analysis, 17 percent say data analysis, 14 percent say computing infrastructure or applications development, 10 percent say protein analysis, nine percent say genotyping or SNPs analysis, seven percent say DNA sequencing, and the remainder are dispersed in jobs that include automation, epidemiology, cloning, quality control, robotics, separation technology, and structural biology.

1,521 GT subscribers responded to our exclusive 20-question salary survey

11% of respondents say their last employer is out of business

46% of respondents say their employer has had layoffs in the past year

4% of respondents have been laid off in the past year