Section 1.1: The Interview Journey

Before sharing my day-to-day activities, I want to briefly discuss my interview experience for a data scientist position at a corporate firm.

The interview process largely mirrors that of many other candidates. After submitting my application, I was invited to participate in a technical assessment, which included tests in Python and SQL. Although I excelled in the Python assessment, I struggled with SQL and was unable to submit a working solution. Surprisingly, I was still invited for further interviews with HR and business stakeholders. When I inquired about this, I was informed that my coding was deemed satisfactory, and programming skills aren’t the sole evaluation criterion for data scientists.

The interviews progressed smoothly, starting with a user interview focused on my previous experiences, aspirations within the company, and general questions. This was followed by an HR interview to gauge my personality and discuss salary expectations.

Subsequently, I was tasked with a data science project that needed to be completed and presented to business users within a week. This project involved creating a machine learning model and explaining the interpretability of its predictive features. Detailing my workflow and outcomes was not overly challenging, as analyzing data and deriving insights had become second nature to me.

Ultimately, this led to a job offer, which I accepted after a brief delay to finalize my previous employment. The entire application process took about a month—a relatively swift timeline for a corporate setting.

Section 1.2: Day-to-Day Responsibilities

Having worked in both startup and corporate environments as a data scientist, I can draw contrasts between the two. However, this article will focus solely on my corporate experiences. My time in corporate has been more enjoyable, not only due to the salary but also because of the immense learning opportunities. This learning encompasses not just technical skills, but also soft skills like communication, project management, and teamwork.

My responsibilities as a data scientist extend beyond merely developing machine learning models or cleansing data. To clarify, I categorize my main duties as follows:

1. Project Management for Data Science Initiatives

   Managing data science projects involves more than just technical tasks; it encompasses all aspects associated with the project. Each data science initiative is initiated to tackle a specific business challenge, meaning that projects start with a defined problem.

Business users often approach our team to discuss issues they need resolved. Together, we brainstorm how to leverage data analytics to address these challenges. A significant portion of my time is spent in meetings, as corporate environments differ greatly from startups. In startups, technology is the driving force; in contrast, corporate settings often require data scientists to continually demonstrate the value of their machine learning models and validate their impacts using business metrics.

A considerable part of my time is dedicated to understanding business needs and presenting results in a manner that ensures comprehension.

Administrative responsibilities are also a key part of managing data science projects. These include ensuring data access, maintaining data privacy, obtaining project approvals, and preparing KPI reports. Given the sensitive nature of data, corporate environments necessitate stringent administrative protocols.

Setting a proper timeline and assigning tasks within the team is vital. Adhering to our plan ensures smooth operations for the business, though balancing technical and business needs can be challenging.

2. Acting as a Data Ambassador

   Being a data ambassador involves promoting the understanding and value of data analytics within the organization. Many corporate employees may not fully grasp the importance of data-driven decision-making, making it essential for data scientists to educate their colleagues.

My role includes conducting training sessions and collaborating with business users. Integrating a data-centric culture can be slow, but over time, colleagues recognize the value of these efforts.

3. Developing Machine Learning Models

   When not engaged in meetings or educational activities, my primary responsibility—developing machine learning models—comes to the forefront. While this is my core duty, the demands of project meetings can impede my development time. Nonetheless, I strive to balance these responsibilities effectively.

Developing a machine learning model requires a comprehensive approach, from understanding the business context to monitoring the model's performance and evaluating its business impact. Although I collaborate with others, the ultimate responsibility lies with me.

4. Establishing Data Science Guidelines

   Although not everyone may have this responsibility, my company has entrusted me with creating standards for our data projects. These guidelines ensure consistency across new and ongoing projects, benefiting both current and future data scientists.

The framework I am developing covers various activities, including model development, data storage, and required explorations. While it serves as a foundational reference, data scientists are encouraged to adapt their methods as needed.

Recently, I have also contributed to the establishment of our company's analytic platform, advocating for changes that enhance our workflows and receiving positive feedback for these efforts.