Blog Post

Day in the life of a Data Scientist

Featuring Ivan Naydenov 


Ivan Naydenov is a Senior Data Scientist (DS) and a Team Leader at Amplify Analytix. He was recently promoted from Data Scientist to his new role.

Although there need not be a typical day, there are certain patterns, types of days, and different expectations of what you can experience as a data scientist

We asked Ivan to walk us through a day in his life as a DS.

Day in the life of a Data Scientist 1

How did you start your day today? 

I normally check my email as soon as I arrive at work and then lay out my schedule for the day, which usually consists of a mix of project-related and non-project work. 

How often do you have meetings VS independent work? 

Taking up a leadership role alongside my senior role meant I had to split my time between project and managerial duties. Due to that I have more meetings now in my work calendar. I would say it has shifted from something like 70-80% independent work to 50-60% right now. As a data scientist I am also responsible for gathering all the required data necessary to solve the business problem after it has been identified. I also meet with stakeholders to understand the many difficulties facing the company, followed by brainstorming sessions with colleagues to determine how we may apply statistical and machine learning approaches to address these issues.   

Does the time zone difference affect your work? 

I am in the Sofia office, so when I work with clients in Europe, the time zone does not really matter to me because there is typically only a one to two-hour difference. Since we are a multinational team with offices in Bulgaria and India, we try to accommodate each other’s schedules when collaborating. We are mindful of scheduling our meetings earlier in the day to accommodate our Indian colleagues. They also usually start their day a little late to allow for more overlap between our calendars.  Our company’s culture places a high value on cooperation and mutual respect between everybody. 

How is your role as a Senior Data Scientist different from your role as a Team Lead? 

As a team leader, I want to provide a collaborative work environment for my colleagues. I aim toensure that my team has adequate resources, and they can work on projects that are challenging but feasible. In this way they can both develop their skill sets and feel satisfied with their work. Additionally, in my capacity as a Senior Data Scientist, I mostly focus on projects while occasionally mentoring junior Data Scientists in my team. 

What is your favorite aspect of being a DS?  

My favorite aspect of the job is the exploratory nature of it.  

A Data Scientist’s job is to use data and algorithmic toolsets to solve unusual problems, which can occasionally be difficult but also very interesting because it may be something that no one has done before in your team (or in general), so you must be innovative. 

Day in the life of a Data Scientist 2

Could you tell me more about these solutions? 

For example, one of the projects that I am proud of, and which proved to be successful with our client was a “Network map”*For context, the client in question sells isolation panels for the construction sector. They have both direct and indirect clients. The direct clients are stores and chains that sell construction materials. The indirect clients are the consumers of these panels, who may be either contractors or architects. 

The client’s marketing team was interested in getting a good grip on their indirect customers, and specifically the architects because they are the ones making decisions on what brands to use for building materials. The client had access to a third-party database of projects around the country where they are operating. The database stores detailed information about building projects – the type of building, who is designing it, who is building it, etc.  

We collected information from the database, and we made a visual map representing the whole building sector in the country. It visualized the connections between the various players in the industry as a network with the architects at its core. A network like this can also be queried for interesting properties. For example, we can query it to identify players who are more densely connected with other players in the industry. This type of visualization is much easier to understand because each company is displayed along with its network of connections.  

*Dive deeper into the “Network Map” solution depicted in our paper in Applied Marketing Analytics Journal by the renowned Henry Stewart Publications.

What intriguing technologies and solutions do you use in Amplify? 

We have developed a product that uses reinforcement learning to recommend bid amounts for search keywords in an online marketing campaign to maximize click-through rates in Amazon and Google ads. This is usually done manually in most companies and agencies while our solution significantly improves results and cuts costs.

Another interesting project involves training a Neural Network to audit the completeness of medical claims submitted to Insurers. This helps hospitals in the US get paid for the procedures they perform, with minimal revenue leakage.

We also provided recommendations to a major retailer on scheduling their staff on the shop floor, for meeting customer service levels at the lowest possible payroll cost. We provided them with a detailed staffing schedule for each day and for each employee based on historical footfall patterns while complying with several legal and business constraints such as min/max work hours per shift and per week, the minimum number of staff on the shopfloor given its layout, etc. 

What is your favorite part about the platform you use for work?  

I prefer Python and I write most of my code in it. Also, it is popular and has a wide support base. Some of the best libraries I need for my tasks are available in Python and receive regular updates. I also enjoy deploying my codes on the AWS cloud platform and creating applications that are useful to my team and our clients. 

What tools help you stay on track daily?  

I would say that time management is crucial for the DS role. To organize better my time and tasks, I take a lot of notes. For instance, if it is the beginning of the week, I plan my daily, weekly, and monthly tasks. I have a massive file of notes that serves as a virtual diary. The other time management tool I use is a stopwatch. I prefer to track my focused time. It helps me, especially if it is a difficult task and I do not know how to approach the problem. I simply start pacing myself with brief focus sessions of 5 minutes and if I achieve that, I increase it to 10–15 minute sessions. It helps me concentrate and get into a rhythm. 

Day in the life of a Data Scientist 3

How do you motivate your team members?  

It is important for my team members to feel that they are heard and that Amplify Analytix as a company will always support them when issues arise. Even if they have difficulties with a project, they know that the team is behind them and that they are allowed to express their thoughts and opinions. That, I believe, is critical, and it is also what pushes me to work in the company. The environment of psychological safety here, helps me be myself and express my thoughts freely without fear of judgment. 

How do you keep a healthy work-life balance?  

It’s been a little more difficult since I became a team leader, but exercise and sleep are helping me minimize stress and stay focused and on track. Because of the unique culture, we have at Amplify, I can achieve the work-life balance that keeps me happy and satisfied while also having the ability to improve professionally, develop, and perform at my best. 

 It is critical for our culture at Amplify Analytix that all our coworkers be pleased. Our Sofia office went to the beach a few weeks ago for some outdoor co-working. 

Dinners, post-work beverages, and beach time were all important parts of our team building. I even tried wakeboarding and windsurfing.   

What are some of the skills you need to have to be a successful DS?  

I believe that the fundamental components are a strong grasp of mathematics (calculus, probability, linear algebra, and optimization), statistics, and basic programming.   Communication skills (both verbal and non-verbal) are also important for a data scientist. Technical ideas may need to be communicated to a non-technical audience in approachable ways. Even though a DS deals with data and figures, their job is motivated by business needs. It’s essential to see the big picture from the client’s perspective, to understand the business context, business problems, and priorities You must be able to explain the science behind the data to the stakeholders, as well as grasp their problems as they see them, not as a Data Scientist sees them. 

 What is your advice to someone who wants to become a DS?  

Data Science is still a young field and the requirements for a position vary across jobs and companies, we are only now seeing some university degrees that cover the building blocks. Even when I started there were no such degrees that combined such elements. , I would recommend starting with a knowledge base of mathematics, statistics, and some basic idea of programming. My non-technical advice would be not to worry if you don’t understand everything all at once because nowadays nobody has a full grasp of all that data science encompasses. The field is vast and constantly expanding. There are people with different specialties – I included. This is not a cause for concern, as long as you are curious and willing to learn.  

It is quite challenging to explain a typical day in the life of a Data Scientist since your daily activities entail creating ML-powered products to address issues that affect business across various industries. As a Data Scientist, you must look for the questions that need to be answered before coming up with various solutions to the issue. if you are interested in constantly learning new things, solving business problems, and having a significant impact on the business world by turning a sea of data into actionable insights, maybe developing a career in the Data Science field could be right for you!