What the Tech?!
Data Scientist at Veeva Systems
Interviewer: Isabella Enriquez
Q. Could you give a brief intro on who you are?
My name is Lediona Nishani and I work as a Data Scientist at Veeva Systems, Vault Safety Product. I hold a PhD in Machine Learning and also mentor people from all over the world who want to enter into the field of data science, or help them land their dream job in the Data Science industry.
Q. Could you give a simple summary of what Data Science is?
Data Science has become an essential part of industry given the massive amounts of data as it helps consolidate them and monetize them to grow their business and increase customer satisfaction. Data Science is the science that uses vast volumes of data to analyze and find patterns, derive meaningful information, and make solid business decisions. It uses modern tools and techniques to achieve its purpose such as machine learning algorithms, deep learning, statistical and predictive modeling, etc.
Q. In your opinion, what are the most important qualities for a successful Data Scientist?
I would say the most important quality of a successful Data Scientist is curiosity. Without curiosity you won't be able to ask the right questions and to understand the business problem. Without curiosity, you won't be able to interrogate the data and get the right answer, or identify the issues that come up during the experimentation phase or deployment. Other important qualities a Data Scientist needs to possess is clear thinking and good judgment. Another quality is being open to change, continually learning everyday, [and] being able to look at yourself always as a beginner. Reading, and reading a lot, is the ultimate superpower.
Q. What are some common misconceptions of being a Data Scientist?
[First, a common misconception of] Data Scientists or people who are starting to learn the field is that they think that they have to KNOW everything. This is a vast field. You're never gonna know it all, and you'll always feel like you don't know enough. That's OK. It's enough to have the ability to be able to UNDERSTAND everything.
Misconception #2: AI will soon replace Data Scientists. Yes, the activities done by Data Scientists manually will be replaced by AI, however a machine cannot know by itself as to what decision it should make to build an efficient model or to work on accuracy of the model. This decision is made from the person who has deep domain knowledge and the right qualifications. Even with the most advanced algorithms, we still need someone with good judgment and expertise to keep the business running. Data Scientists aren't going anywhere, their job is evolving.
Misconception #3: Tools are all you’ll need to become a data scientist. Tools are coming and going, critical thinking and curiosity [are] skills that need to be developed and cultivated. Skills such as problem-solving, a good grasp of the concepts, [and] information regarding the correct applications required for a business problem are also important to be mastered along with mastering tools.
Q. What's some of the biggest challenges of being a Data Scientist?
There are a variety of challenges that I come across during my work as a Data Scientist. Sometimes the data cannot answer the question that we are asking, [so] we need to find other data sources from the customers, which might be challenging if we are building a POC because the customer has not been landed yet.
Sometimes clients are not able to define their KPI or there is a lack of clarity of the business problem, which might come from other stakeholders in the team who are not clear as to what they want to achieve with Data Science. If the expectations from Data Science implementation are not aligned with the end-goals, then the project is destined to fail.
Collaborating with other departments might be challenging as well, they might not be data-literate [and] therefore they have a hard time understanding the value of Data Science–it is our responsibility to help them understand and talk their language, which sometimes is hard to master.
[When] learning the domain where you are applying Data Science, there are chances that the metrics used do not serve the purpose of implementing DS. Learning Data Science includes not only knowing development of algorithms, but also requires a keen understanding of other practices. This consists of a mix of metrics and KPIs that boost business growth.
Q. What do you enjoy most about working in Data Science?
I do enjoy the research phase a lot–when we bounce off ideas as a team to define the business problem. Immersing myself in a new problem and trying to find the answer with Data Science is really exciting to me. Collaborating with various stakeholders, such as the Product Manager and devs, and having constructive and productive conversation, and learning their perspective is something I enjoy a lot.
Another thing that I love doing, is presenting the ultimate outcome to the client and making sure that they are satisfied with our product solution.
Q. What advice do you have for students who want to go into Data Science?
[Firstly], You don't have to know everything–start with the basics, learn the fundamentals really well, statistics and classic machine learning algorithms. Keep it simple, less is more.
[Secondly], don't spend too much time searching for the best course. Learn by doing instead of sitting watching videos. At the end of the day, it's all the same material. Find a course/format/instructor who YOU enjoy learning from. And stick with that person. DO STUFF. For every hour you spend learning, you need to spend three doing. If you see an example bit of code in a course, don't just run the line and be done with it. Tweak the code, play with the input, change the code and see what happens.
[Thirdly], create your Data Science supporting system. Choose your mentors, surround yourself with people in the field who you admire and you look up to. Don't forget we are the average of 6 people we spend the most time with. Choose who you spend time with wisely.
[Finally], build resilience, learn from your mistakes and be able to pick yourself up from rejections of interviews (there will be lots of them in the beginning) or a failed project that didn't work as expected. Learn from every experience and iterate and do better, apply your learnings. Dust yourself off and keep moving forward.