My name is Ruxandra, and I embarked on a journey as a Data Scientist Consultant at one of Rockborne’s biggest clients. My goal coming into the role was to leverage my data knowledge, gained through Rockborne’s training, to improve business efficiency for the company I was placed with. Over the course of one year, I had the opportunity to drive optimisation projects, as well as support data analysts with report generation. In this blog post, I will share my experiences, lessons learned, and skills I acquired during my placement.
Streamlining Data Analysis Processes
When I first started, I experienced a mix of anticipation and nervousness, as I was the only technical person on the team, and I knew I had to bring value through my data science knowledge. One of my first responsibilities was familiarising myself with the team, understanding their work processes, internal technologies, software operations, and identifying areas where I could make valuable contributions.
I soon learned that one of the biggest challenges that our team faced was dealing with burdensome PDF files that contained important information. These files hindered the data analysis process due to their lengthy and unstructured format. Recognising the need for a more streamlined approach, I took the initiative to develop a solution using my Python skills.
I created automated processes that converted these large PDF files into clean, structured Excels sheets. This transformation enabled management to extract necessary data and generate valuable insights. The impact was significant, as it not only saved two hours per day per person, but also enhanced the accuracy and reliability of the data analysis. This automated process is now used daily and will be soon deployed to other internal data teams to be incorporated and adapted to their needs.
Collaboration and Stakeholder Management
I learned two core lessons during my time on placement.
The first lesson was the importance of building relationships with co-workers. While I worked independently on my Python projects, I still had many opportunities to engage and collaborate with fellow data scientists within the organisation, which helped me enhance my skillset and enriched my understanding of Python.
Networking with the team not only helped me polish my code, but also enabled me to gain a deep understanding of the overall business operations, and how my various projects could support various business goals in different departments.
In other words, building connections with other data scientists gave me a chance to expand my project’s reach and contribute to future optimisation processes within the business.
The second lesson I learned was the importance of speaking in your stakeholder’s language. Because I worked autonomously on this project, I learned the importance of effective communication, and how to share project details, objectives, and final outcomes in a way that was clear and understandable.
Particularly, when addressing the implementation and usage of my Python project, I made a conscious effort to articulate the key steps in a simplified manner, minimising technical terminology. The goal was to foster enthusiasm and receptiveness, while also preventing my stakeholder from feeling overwhelmed by the execution of Python scripts.
Key Takeaways and Conclusion
In summary, my placement experience proved to be immensely valuable. I had the opportunity to acquire and put into practice new skills, collaborate with established data scientists and business managers, and make meaningful contributions. This experience gave me a sense of confidence in my ability to drive operational improvements through data, alleviating the constraints imposed by conventional approaches and software systems.