Interview with Heather Krieger, Principal Data Scientist and Team Lead, Savi Technology
We thank Heather Krieger from Savi Technology for taking part in the part of the Data Science Interview Series 2020 and sharing her story of how she got into data science along with her views on ethics and data science and several insights and advices for beginner data scientists.
At what point did you realize that you wanted to pursue a career in data science, and how did you get into it?
I stumbled upon data science. I was in a PhD program and was starting to apply to post docs and academic jobs, but the market was/is rough, and I was trying to coordinate geographic locations with my spouse. An email soliciting applications for a data science fellowship/bootcamp popped up in my inbox and I decided to apply for it. Did a bit more reading into what data science is and what a data scientist does and decided it would be a great fit for my skills and what I wanted to do. Bonus that it made finding a job a lot easier.
How is data science used to create value in your current project(s)?
I work in supply chain technology. My team and I create algorithms to predict estimated times of arrival (ETAs) for global shipments across transportation modes and through stops like ports and customs. These algorithms detect anomalies in shipment behaviors and identify risky behaviors and locations for high-value goods.
Using data science in supply chain analytics helps companies answer key questions like “Where is my shipment and when will it arrive,” and be more efficient in their operations. Data science is emerging as a big differentiator in logistics, enhancing in-transit visibility making companies proactive rather than reactive.
The Savi team works closely with our customers to identify their specific supply chain issues and provide data-driven solutions that increase the efficiency and security of their operations while decreasing the cost of shipping and loss of goods. For example, one manufacturer we support was losing fragile product due to breakage in-transit. We were able to determine which carriers and methods of transport were resulting in significant loss. Our customer saved millions of dollars in product by moving their shipments to safer transportation modes.
What is one of the best investments that has propelled your data science career the most?
The bootcamp that I was accepted into and completed in person was fundamental in making my transition from academia to data science. I already knew a lot of statistics and research methods but had to learn the coding and big data wrangling practices. Learning in a group of 10 other students was invaluable. The program was free but did require moving to D.C. and did not cover living expenses. It was an intense 8 weeks but I learned a ton, met great colleagues who became good friends and sounding boards for problems, and was coached on getting jobs in data science.
What are the top challenges...