Laboratory research careers

Within a lab context, you can choose from three career paths: discovery research, bioinformatics and animal sciences.

Discovery research

Since biotech is still in its infancy, most jobs in biotech companies, especially the smaller ones, are in discovery research. Discovery researchers can range from protein chemists to geneticists to biochemists to many other disciplines in the life sciences. There are jobs at all levels. With a Bachelor's, you can get an entry-level job as a research associate and work for several years, tough, though you will need an advanced degree for more senior jobs. Most responsible positions, however, require a PhD. You can definitely break into the industry after undergraduate studies. Entry-level research positions will get your feet wet and give you a chance to experience the culture of research first-hand before committing yourself to advanced studies.

Animal science specialists

Instead of using chemicals the way traditional pharmaceutical chemists do, discovery research scientists use cells, which have to be obtained from animals, cultivated, separated and utilized in special facilities. Discovery researchers rely on veterinarians and other animal science specialists. They grow cultures, make and purify DNA, and help conduct the earliest phases of testing, when a drug's safety is determined via animal testing.

Bioinformatics

Since nearly all experimental setups are computerized and reams of data are generated with each experiment, the results of biotech experiments are analyzed by specialists who straddle the fence between the biological sciences and information technology. These data analysts are called bioinformatics professionals and comprise some of the most sought-after employees in the industry. They help discovery researchers identify those molecular structures that have the most favorable response profile, and thus the most promising drug candidates.

Bioinformatics has three realms of activity: you can create databases to store and manage large biological data sets, you can develop algorithms and statistics to determine the relationships among the components of these datasets or you can use these tools to either analyze or interpret biological data - e.g., DNA, RNA or protein sequences, protein structures, gene expression profiles or biochemical pathways.