Commentary: The worldwide biotechnology job market: New technologies drive biotechnology employment upward
Bibliographic record
Abstract
Employment trends in biotechnology tend to resemble the big roller coaster at Disneyland. Although the overall direction is always upward, the current situation usually reflects some dramatic shift or another. There are always ups and downs for biotech careers. Despite this, most consultants would agree that the current situation is very positive, even though this is not always reflected in the stock market. Consultant Roger Shamel believes that worldwide biotech employment trends look bright, and he predicts significant growth. “We see the current worldwide employment at 255,000 in biotech companies worldwide. When you take into consideration the work done in pharmaceutical firms, government labs, and private research institutions, that number climbs to just over 400,000,” says Shamel. His company, Consulting Resources Corporation (Lexington, MA) was founded in 1981 and remains a leading consulting firm focused on the global biotechnology industry. The following is a breakdown of the Consulting Resources Corporation worldwide biotechnology company employment numbers: USA, 175,000; Europe, 45,000; Canada, 20,000; remainder of the world, 15,000. Despite the growing biotech job market, employers require a great number of very different skill sets. This means that the actual job market is quite fractured. Although both the molecular biologist and the chemical engineer are necessary biotech jobs, the current need for them varies with the state of the industry and the specific employer. The impact on biotech employees is that certain skill sets will be hot or cold at any one time, despite overall growth in the job market. Biotechnology follows this path of “niche” job growth, because that is what the nature of the product development demands. It takes a very diverse group of scientists, engineers, regulatory specialists, clinicians, and business people to drive a new product forward. Unlike other high tech fields, information technology for example, biotechnology requires these many diverse jobs. Although the growth of the information technology industry might be estimated solely by how many programmers will be required, there is no such easy method for forecasting future job markets in biotechnology. Workforce development officers in economic communities from Europe to the United States struggle with this question every year. They know that their biotech initiatives will require dramatic increases in certain skills, but there are always surprises because of the way that technology unfolds. In a recent survey of independent recruiters who work in the worldwide biotechnology industry, both European and American firms noted the huge increases in hiring that have come as the result of the genome effort. They report that these have resulted in many new job categories in the drug discovery area, which is currently in the lead position on the “hot jobs” list. This recruiter survey generated a current list of jobs that human resources consultants believe to be the hottest prospects in the current biotechnology job market. Drug Discovery (bioinformatics, proteomics, high throughput screening, assay development, medicinal chemists). Drug Development (process development engineers, fermentation specialists, protein purification specialists, process chemists, formulation specialists, drug delivery specialists). Operations (manufacturing, quality control/quality assurance, validations specialists, bioanalytical chemists). Regulatory and Clinical (clinical research associates, regulatory specialists, Chemistry Manufacturing and Controls specialists, project managers). Business (business development, licensing, technology transfer, intellectual property attornies). Consulting Resources Corporation expects the total worldwide employment numbers to increase to 750,000 by the year 2011 (400,000 of those employed in biotech companies). Major worldwide recruiting firms are also certain of this kind of growth, but noone is willing to go on record about the specific areas of technology from which these jobs will be generated. The biotechnology industry continues to break new ground, creating products and future jobs that will come from bursts of new technology development. To tens of thousands of worldwide biotech employees, it remains a fascinating and yet challenging industry for a career.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.088 | 0.078 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".