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Changing Careers to Become a SAS Programmer in the Biotech / - page 2 / 9





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This paper evaluates various different paths that individuals took on their road through clinical SAS programming. There is no one correct path but rather a diverse set. This demonstrates the unique combination of skill sets needed to thrive in this environment. This position has only recently been established so many of the individuals who entered this field did not set out to become a clinical SAS programmer. For many, the journey was unplanned and there were no road maps. They were pioneers and navigated their career path following their intuition and relied on luck. There are many lessons that can be gained from these pioneers. The paths which these pioneers paved will provide a clearer vision and set of directions for future aspiring SAS programmers who plan to venture on the same roads.


It is not about how good a SAS programmer you are. It is about being focused on the data and your representation of the data that leads to success.

Starting Out

When David started out on his first SAS programming position at Syntex in Palo Alto, California in the 80s, this type of work was not well understood nor well known on the west coast. The term ʺBioanalystʺ used at Syntex for statistical programmers was foreign and he did not know what to expect.

David went to UCLA to obtain his bachelors degree in Mathematics. He then continued his studies at Fresno State in Biology and did his thesis work in Lymnology, or the study of fresh water ecosystems. His academic experience did not include formal computer science nor teach him how to program in SAS, but it did include exposure to research methods and statistics that he used in his thesis. In fact, David had little formal programming training. He was introduced to programming when he took a FORTRAN course at UCLA. Back in the days of punch cards, he used FORTRAN for an ecosystem modeling class in graduate school. Out of personal interest, he learned BASIC programming on his own while working as a Lab Technician at U.C. Berkeley. As he developed his career, the technical aspects of programming were merely a means to an end. That end was the data itself and how the data demonstrated meaning when analyzed. The meaning of the data was therefore pivotal and overrode the mechanics of getting there. David gained enough proficiency in programming on the job to perform well in his job without much formal training.

In 1979, David moved to Walnut Creek in the east bay after he had completed his course work in Fresno. He explored jobs in the bay area and first worked in the department of Materials Science at U.C. Berkeley helping on research projects aimed at finding ways to utilize coal dust. As that job was ending, he found a job announcement at the U.C. Berkeley placement center for a Bioanlyst at Syntex. Syntex was an established pharmaceutical company, but due to the lack of experienced statistical programmers in the bay area at the time, they hired people at entry level. Although he lacked the job specific experience, he applied for the job at Syntex because he was intrigued by the blend of statistics, health research and programming mentioned in the job announcement. At the time, however, he did not know what a Bioanalyst did or what the job entailed. The commute from Walnut Creek to Palo Alto required him to go over the east bay hills, across one of the bay bridges. This added to his commute totaling about 60 miles. The job had to prove to be a good fit for him to keep up with this grueling commute, even with the help of a carpool. In the end, it was the research aspect and ability to understand the meaning behind that data that cemented his decision to stay in this field as a career.

He began to appreciate the goal of producing study reports summarizing the safety and efficacy results of clinical trials. While he enjoyed programming and developing good SAS programming skills, the output produced by his programs were more important than the SAS programs. He began to realize that this was the pivotal aspect of the Bionalystʹs job. The Biostatistician, who was the Bioanalystʹs client, wanted to receive quality output tables representing the study data and its analyses results. They were not interested in the quality of the SAS program used to create the tables, so long as they produced accurate results.

The understanding of the disease that he was working on was another fascinating aspect of the job. He is not clinically trained as a MD or a RN, but his interest in research and learning about the disease related to a given drug allowed him a unique view of the data. This was a window through the data which provided meaning that became essential in his work. The interactions with other team members mostly involved communicating with the Biostatistician. However, it also included interacting with the other clinical team members, such as the MD in charge of the project. The Biostatistician and Bioanalyst worked together closely in analyzing and producing report tables and graphs. Even though the meaning of the statistics is in the domain of the statistician, it often ended up with the Bioanalyst needing to interpret their instructions to fill in the gaps. This multifaceted aspect of the job kept David interested in the work which sustained and rationalized his long commute for 12 years.


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