With a degree in marketing and working in the field for over a decade, I have had experiences with companies small (< 50) and large (50,000+). None of this truly prepared me for the technical program management of a marketing campaign in a FAANG environment.
To give a high level, here’s a list of the teams/skillsets represented — each of these being filled by a different individual and many times multiple are needed for a single program:
…and this is just the core team on day-to-day needs.
It’s been almost 6 months since I began my data science education, and we are now at the point of having opportunities to apply our skills outside of classically assigned datasets. This means I get to learn the valuable skill of finding fitting datasets to practice coding on, not only for the fun of development, but as a “choose your own adventure” project. In this case, we are focusing specifically on applying classification modeling.
For those reading this outside of the data science space and are asking
“but what does classification modeling actually do?”
In my last post, I introduced my journey into learning data science and why it was important to me. Since then, I have been developing foundational skills in Python and SQL to be able to apply this knowledge. My first opportunity to apply those skills was our Phase 1 project.
The assignment put us in the mindset of a consultant tasked with providing recommendations to Microsoft. What could Microsoft possibly be needing consultation on? Well, they want to start a movie studio.
I’ve always been one for numbers, building, and making an impact.
With a decade of experience in Sales and Marketing Operations and a passion for building efficiencies for businesses small and large, data science is a complementary skill to up-level the possibilities available with this end-to-end data to make Sales and Marketing teams work smarter not harder while (more importantly) creating a positive customer experience.
On a more personal note, my biggest passions are: