Time to Reflect: Our Culture From a Maternity Leave Perspective

Bonuses and monetary incentives are touted as a cure-all solution for instilling a culture in a company. Fortunately, it will take a touch more than financial incentives to get the culture right. Giving monetary incentives can lead to poor results; look no further than what happened to the Brits in colonial India. The British governor of India was concerned about the number of venomous cobras in Delhi. This mundane problem led to a government’s clever scheme: a bounty for every dead cobra brought to the authorities. The local population went hunting and, no surprise, got the Brits lots of dead cobras. The incentivized outcome was achieved. However, given the big picture misalignment between the oppressed local population and the British rulers, cobra hunters didn’t mind being a bit entrepreneurial. They started to breed cobras and hand them to the government. The reward program was scrapped when the government realized its incentive structure was flawed. Cobra breeders set their now-worthless snakes free, and the wild cobra population exploded. 

Employees are the seeds of company culture. As founders sow, so they will reap. The incentives employees are watered with makes a difference in what kind of culture the company evolves into. Are employees showered with freedom, knowledge and trust? Some would think that money speaks volumes and makes up for the gaps in other areas. 

While it is challenging to address relative values such as trust and freedom, which might mean different things for each of us, I would like to highlight four values that we in Things Solver honor, and I believe they make a difference in our work environment.

Honest feedback

Honest feedback is much more than voicing an opinion or filing an annual 360 review. Excellent feedback is hard to write. It should be genuinely helpful to the recipient, suggesting an alternative perspective or a solution to the problem. 

We are not used to being proven wrong. It takes time to start appreciating honest feedback, especially unsolicited feedback. Ego has to step aside. The culture of the company is morphed in such moments. Either the input is cherished, and the person voicing it is praised for her desire for mutual progress, or the path of least resistance leads the culture towards an unhappy place. If the management silences the critics, the culture steers towards messengers of good news, while the bad news is slipped under the rug. 

The first feedback I sent to my line manager was regarding a management’s decision, with which I disagreed. I digested it overnight and was comfortable with my perspective. Still, I was weighing whether my feedback would be appreciated. This was very stressful. 

My manager proved her salt and showed me that everyone’s feedback was welcome. More than just platitudes, she made me feel considered and respected. She did the most challenging part of giving feedback – receiving it. Ultimately, the management did change their decision. 

It empowered me to step forward with feedback every time I feel the need. The effect of such a situation on the company’s culture is permanent. 

Fresh sets of eyes

The beauty of my profession is that it is advancing at such a rapid pace. When we hire, we don’t look for a degree in data science or data engineering. Such a degree doesn’t exist. The vast majority of the knowledge needed for the job is learned by observing and doing. 

The discovery is not about finding new landscapes but about finding new sets of eyes.

We hire for cultural fit. A newcomer that has a background in a seemingly unrelated field frequently proves valuable. My experience in finance and economics helps me understand how our client’s businesses work. My colleague’s background in psychology helps us understand why customers of our clients behave as they do. My colleagues’ experience in engineering allows us to integrate our ideas on solid ground. Diverse backgrounds bring fresh sets of eyes. 

A client might have a predetermined view of his business and its data. Our job is to approach each project and its data with a beginner’s mind and leave our biases behind. It is an everyday practice that requires us to be open-minded. 

Over time, this becomes a natural way of thinking. 


Work on live client projects can be as good as the relationship between the client and us. The best results are achieved when there is alignment between client decision makers and our team deployed on the project. Failures happen, and they are very welcomed, but it is unacceptable not to learn from them.

Very quickly upon joining TS, it was clear to me in which areas we excel. I reached out to a friend at one of the leading companies in FMCG and invited him to discuss how we can help them make better decisions using their data. 

We presented what we thought we could do. The potential client seemed interested, at least to me, who hoped they would be. However, they didn’t seem to ask the type of questions I used to hear on our successful projects. Nevertheless, this was my initiative, and Things Solver management gave me the go-ahead to work on proof of concept with the potential client – a three-week investment of our resources to prove our data delivers tangible results. 

As time passed, I felt that the potential client wouldn’t commit their resources to the project. I didn’t realize we were wasting ours. After two weeks, management supported me to thank the potential client and redeploy our resources elsewhere. 

The experience was stressful. I learned the feeling of putting a square peg in a round hole and have no intention to repeat it. The sense of autonomy the team instilled in me was the only way to fail and learn from it.

Over time, I realized that an open-minded approach to failures is a lasting trait of our culture. 

Stay in sync 

Like a good jazz orchestra, high-performing teams don’t play to predetermined tunes. They stay in sync and play as they go. Staying in sync means understanding where each team member is coming from and how they currently perceive the situation: the larger the team, the exponentially larger the necessity to stay in sync. Leave matters open to interpretation, and the team chemistry can disintegrate. 

Last year was a year with no spontaneous syncing next to the watercooler. We might have underestimated the power of daily meetings before 2020. We’ve used all the tools and skills at our disposal and worked hard to stay close to how our team members, leadership, and clients feel and think. 

Staying in sync helps all newcomers integrate and keeps our goals aligned. It will remain a cornerstone of our routine in the years to come. 


Our empowerment of honest feedback, discipline to stay in sync, open mindedness towards failure, and insistence on fresh sets of eyes, make me excited to reunite with the ever-growing team after months of maternity leave that offered me time and distance to put our culture into perspective.  

Friday talks: A Data Science Project

This post is not going to be about another Data Science course you should enroll in. It’s not going to be about various skills you should build in order to develop a Data Science project, either. Considering the title of this post – A Data Science Project – I tried to create a pun. Your journey to the destination called “I am a Data Scientist” is a project you should be working on, with phases, iterations, and disagreement between the user requirements and generated outcomes. I would like to talk about my Data Science path, and what it is like to be a Data Scientist from my perspective. I can assure you that there are tons of blog posts on the web that are sharing the same topic, enriched with more information and experience than my own, but the thing is – I want to talk about heading this way and share with you some unconventional directives that made my journey a lot easier, and hopefully, would do the same for you.

So, regarding the beginnings, there are some baby steps you should make, in order to build basic skills needed for the purpose of analysis and extracting insights. And you really do it well. The beginnings are no longer a problem. Most of you start with the Machine learning course held by the incarnation of a deity in the world of machine learning, Andrew Ng. Or with DataCamp. Or at Kaggle. And that’s the right way to do it. But there are some additional activities you can practice, that will make it a lot easier for you to master this field and/or to enrich your experience and spread your collection of skills.

1. Research & Blogs

Being a Data Scientist requires lots of research. In order to extract the most possible from the data, you should be aware of the limits. And the limits are constantly changing. How to know where the limit is? By doing some serious research! Follow what’s the academia doing, but also how is the implementation going in the industry. Besides academic research and scientific papers on some particular subject, I read lots of blogs on a daily level. Some of the blogs that I personally like are Analytics Vidhya, Towards Data Science, Machine learning mastery, Brandon Rohrer’s blog, and Colah’s blog. Or, you can install Flipboard, set your topics of interest, and follow up.

2. Meetups & Conferences

Communities and gatherings are some precious things in this field. Lots of enthusiasts and experienced people can be found on such events, sharing their knowledge and findings. At Things Solver, we really believe in the “sharing is caring” idiom, and with that in mind, we try to share our knowledge and to let it grow even more through these events. There are many meetups in Serbia with Data Science, AI, and related topics, so you can start with exploring the Meetup.com and areas you’re interested in. The most popular Data Science community is Data Science Serbia, organizing meetups, usually encouraging bonding and networking of Data Science enthusiasts. As for the conferences in Serbia, the most popular one for certain is Data Science Conference, growing bigger each year.

3. Social networks & Influencers

Social networks are a good way to follow the activities and events, even though you’re not able to be there physically. What I really use on a daily level is LinkedIn. There are some inspiring people that I follow and learn from, like Favio Vázquez, Brandon Rohrer, Jason Brownlee, Andriy Burkov and many, many more.

People are often underestimating these things, but they really are a crucial part of a continuous Data Science path. And that is one of the biggest problems one encounters at the beginning. Like every other field, it requires dedication, research and lots of learning. And, since it is continuously growing, one should simultaneously grow alongside, in order to be at the top, comfortable with the cutting edge technologies. And, to be honest, that is not easy.

The wanderer’s puzzle

The first thing I want to discuss is something I call “the wanderer’s puzzle”. And I want to open this section with the Tolkien’s words ”Not all those who wander are lost…”. So, entering this field (or any other field), you’re probably feeling lost. But what’s the right thing to do Data Science? It depends. There is no such thing as a recipe with perfectly determined doses of ingredients. The first thing is to wander. To find yourself. And I have a really interesting story to share with you, called The Hedgehog and the Fox. My dear colleague Anđela shared this story with me, and it really helped me find myself. I pulled the analogy regarding this topic and Data Science. You have to determine whether you’re a hedgehog or a fox. It depends on your interests. You can either be a hedgehog, focused on mastering one thing, or a fox, squirming thought various domains at the same time. Regarding the Data Science, I know many colleagues that are totally hedgehogs (they are experts in computer vision, for example, but they have never heard of Isolation Forest or a Survival curve). And similarly, I have lots of colleagues who are foxes, they have played with CNNs, time series analysis, store optimization in various domains like marketing, finance etc.,  but they always say they haven’t yet dug any of these areas deeper.

The imposter syndrome

Another thing that I would like to talk about is confidence. Reading lots of blogs, listening to many technical courses and presentations, I’ve really had hard times believing in myself and building confidence and self-awareness. Never thought about the real problem I was facing – called the Imposter syndrome. So, the imposter syndrome… This is a situation where you’re doubting yourself, your competences and knowledge, afraid of being exposed or flagged as a “fraud”. This is a frequent problem, and lots of successful people are facing it. You know that there will always be someone with more experience, more knowledge, better competences. That’s not the problem. The problem is that you think you’re not good enough. That your acknowledgments are not yours, but the merit of someone else, or accidental series of happy circumstances. And that it’s only a matter of time when someone will break you and ruin your career and everything you’ve accomplished. I was lucky to have a conversation with a more experienced Data Scientist, who pointed me to this problem. And I have a perfect read on this topic here. So, stop doubting yourself and keep rocking the Data Science!

Development vs. production

When looking at the practice and the real-world application, there also are some key drivers you should be aware of, in order to keep up the trace and save your stamina. And that’s not something that you can easily learn or hear about just around the corner. Dealing with some real-world Data Science projects, I have learned one crucial thing. You should never (like, EVER !) look at the Data Science project development and production as two separate things. They are done in separate phases, they can be done by separate teams, they can eventually be separated by the environments and the conditions they are running in. But they should always be regarded as a whole, a unity, a completeness. Now, I know that you’re asking yourself – why would I possibly divide those things – yet again, I am sharing my experience, and yes, I made this mistake. And learned from it.

Each Data Science project starts with a problem that should be solved. The solution of the problem should lead to business improvements, reflected in revenue increase, cost reduction, or whatever the desired metric is. There are several phases in the development process, as well as in the deployment, and this flow is usually divided between several teams. Due to the numerous phases and iterations in the process, lots of things can happen, potentially leading to complications and project failures. It is unnecessary to emphasize that everyone involved should be completely dedicated and aware, for this process to be perfect. So, is there anything that you can do (or avoid doing), as a Data Scientist, in order to make this process as fluent as possible? Yes, for sure! In many cases, Data Scientists are described as lazy and messy. Why is that? We develop our models and test it in some environments that are not even IDEs, but some kind of a browser tab! We love the interactivity and line by line execution! And that really comes in handy during the development phase, when playing with the data and different models. We have a pretty narrow focus on finding the right model, putting everything else (like data withdrawal, code modularity, results delivery, etc. ) aside. The problem appears when you’ve chosen a satisfying model. You cannot just throw it around to the teammate assigned to the deployment, like it is a hot potato! And that’s the biggest issue in every project. In most cases, especially when you are rookie, models are not production-ready. And it can lead to lots of headaches. Data Scientists often neglect the steps that are coming after the model training phase. And that is pretty irresponsible and not aligned with the team spirit you should have! You have to think about production and model deployment. You have to communicate with the ones responsible for the model deployment. And, if it’s you that is also deploying the model into production, you should be responsible to yourself, too.

My most sincere recommendation is to always think about the whole process. The things you should always take into consideration are model scalability, generalization, adaptation, optimization, and additional tweaking. Write code that is readable and easily upgraded. Parameterize everything that is prone to changes. Develop models that can easily be enriched with more data. Create pipelines. And, even if you’re a researcher or a “lazy” Data Scientist in a team consisted of both Data engineers and Machine learning engineers, make sure that you understand the whole process, at least. You’re not an independent entity in the project. The process will be much faster and more efficient if you take these into account from the beginning. And not to mention the project flow and success rate. Finally, you are a Data Scientist. It’s not only about 95% accuracy. It is about the impact of the whole process. You have to understand why you’re doing it. But also how that is changing the environment you’re in. And that is much more satisfying than the 95% accuracy, to be real. If a model with 68% accuracy is driving the changes and creating the business value – I’ll totally be up to that!

There is one last thing I want to share with you. How do you continuously grow? The following are three very simple, but powerful steps I stumbled upon while browsing the net (check out the whole post here, it really is valuable).

Identify your weaknesses

Define a plan that should convert your weakness to your forte

Execute the plan


Simple as that, ain’t it? 🙂