Time and again, the data science industry has been the talk of the town. But do you know what goes on in the life of a data scientist?
While many articles will tell you the roles and responsibilities of data scientists, what are the challenges they face on a day-to-day basis? And what skills and tools they use; fewer articles will take you deeper into what a typical day for them looks like.
For a lead data scientist, a typical day depends on the phase of the project he or she is working on. But, on a high level, these are the things one would do in a day in the role of a lead data scientist.
Let’s look at each one of them closely:
The day picks off with a cup of refreshment while checking emails. This is the first thing to do as the day begins is to ensure nothing important was missing from the previous day. These emails often contain updates from the team’s machine-learning models and feedback from stakeholders.
Additionally, the lead data scientist utilizes a tracking task-tracking tool called Jira to update the status of the tickets they are currently working on for the upcoming weeks. This process, commonly known as a sprint in the agile software development realm, enables them to effectively prioritize their daily tasks. By doing so, they maintain organization and focus, ensuring they are making progress in the right direction.
After having visited the emails, the day leads to a quick meeting with the team to discuss and address any issues that they are facing. A review of work, discussing any roadblocks or challenges, and prioritizing the tasks for the day ahead is what a team meeting looks like. As a lead data scientist, it is essential to ensure that everyone in the team is on the same page and working towards the same objective.
Data Analysis and Modelling:
This involves working with extensive datasets, using statistical methods and machine learning algorithms to discover insights and build models that can foresee future outcomes. Depending on the stage of the process, there might be a need to clean and pre-process the data, do feature engineering, perform model selection, and validate.
A lead data scientist would want to spend some time on experiments. This involves setting up experiments, running simulations, and analyzing the results. The aim of these experiments is to test different hypotheses and fine-tune models, making them more accurate and effective. This is a highly technical aspect of this job and requires a deep understanding of statistics and machine learning algorithms.
Meeting with stakeholders:
A lead data scientist is often found working on multiple projects. With numerous projects comes the need to attend to various stakeholders. The day involves meeting up with different stakeholders from within the organization, like the marketing team, sales team, or finance team, to name a few. The purpose of these meetings is to showcase the findings, discuss any concerns, and ensure that everyone is aligned on the next steps. These meetings are an essential part of the job as they help to ensure that the work is done effectively.
An important aspect of meeting with stakeholders is to explain the data science results. The model results are not always aligned with the business, and bridging the gap is a necessary skill to ensure people get confidence in the product.
Review & Mentorship:
After a break, the day resumes with code reviews, providing feedback on the team’s work, and guiding them to develop new skills. Mentorship is a crucial part of leadership, and this ensures that the team is constantly upskilling and improving in their career. This also helps to overcome roadblocks in the projects and improves confidence to do better.
Research and Experimenting:
A lead data scientist delves into research and experiments, recognizing the need to stay updated with the latest technologies and continuously experimenting to achieve optimized results. This crucial aspect of their role involves exploring new methodologies, algorithms, and techniques in the ever-evolving field of data science. By conducting experiments, they push the boundaries of what is known and seek innovative approaches to solve complex problems. This dedication to research ensures that the team remains at the forefront of advancements, enabling them to harness the full potential of data and deliver the most impactful insights. Through ongoing experimentation, the lead data scientist not only enhances their own expertise but also contributes to the growth and progress of the entire data science field.
As the day draws to a close, a brief meeting with the team can help to close any loopholes or queries that they might be facing. Team collaboration helps to motivate the team to brainstorm new ideas, review code, or discuss strategies for future projects. Collaboration is essential in data science, and it helps to bring out the best ideas by working together.
The work of a lead data scientist seems never-ending, but for the day, the work needs to end, and so the wrapping up of the day happens by checking any emails that require immediate replies, reflecting on what has been accomplished in the day, and what are the tasks for tomorrow.
The day of a Lead Data scientist in a nutshell
A lead data scientist wears multiple hats. Being a leader, the most important task he or she carries out throughout the day is ensuring that the team works towards the set goal and that they have all the means to achieve that goal. The outcome of each project must align with that of the company’s goals – being efficient and cost-effective. In doing so, a lead data scientist may have to evaluate existing systems, develop new strategies, and set up new processes that can ultimately save time and money.
Being a lead data scientist requires constant learning, iterating, problem-solving, and decision-making. It’s not only a technical role but also requires strong communication skills to help promote data science initiatives and solutions and ensure that the results are shared with other stakeholders. With so many responsibilities, successful lead data scientists must be highly organized and can prioritize tasks according to importance.