How to Become a Successful Data Scientist and Data Analyst

There are many articles on the skills required to be a good data scientist or data analyst, but few articles cover the skills required to succeed – be it exceptional performance appraisal, executive praise, promotion, or all of the above. Today we present to you the material, the author of which would like to share her personal experience as a data scientist and data analyst, as well as what she learned to achieve success.


I was lucky: I was offered the position of Data Scientist when I had no experience in Data Science. How I dealt with this task is a different story, and I want to say that I only had a vague idea of ​​what a data scientist does before I took the job.

I was hired to work on data pipelines in connection with my previous job as a data engineer, where I developed a predictive analytics data mart used by a group of data scientists.

My first year as a data scientist involved creating data pipelines for training machine learning models and implementing them into production. I kept a low profile and did not participate in many of the meetings with marketing stakeholders who were the end users of the models.

In my second year at the company, the data processing and analysis manager responsible for marketing left. Since then, I have become a protagonist and have been more actively involved in developing models and discussing project deadlines.

As I communicated with stakeholders, I realized that Data Science is a vague concept that people have heard about, but do not quite understand it, especially when it comes to senior management.

I have built over a hundred models, but only one third of them were used because I didn’t know how to show their value, despite the fact that the models were requested in the first place by marketing.

One of my team members spent months developing a model that senior management felt would demonstrate the value of the data scientist team. The idea was to extend this model to the entire organization once it was developed and to encourage marketing teams to apply it.

This turned out to be a complete failure, because no one understood what a machine learning model was and could not understand the value of its application. As a result, months were wasted on what no one wanted.

From such situations, I have learned certain lessons, which I will give below.

Lessons I Learned to Become a Successful Data Scientist


1. Set yourself up for success by choosing the right company.

During your company interview, ask about the culture of the data and how many machine learning models are adopted and used in decision making. Ask for examples. Find out if your data infrastructure is set up to start modeling. If you spend 90% of your time trying to pull out raw data and clean it up, you will have little or no time to build any models to demonstrate your value as a data scientist. Be careful if this is your first time hired as a Data Scientist. This can be both good and bad, depending on the culture of the data. You may face a lot of resistance when implementing the model if senior management hires a Data Scientist just because the company wants to be known as using Data Science to make better decisionsbut has no idea what that really means. Plus, if you find a data driven company, you’ll grow with it.

2. Know the data and key performance indicators (KPI).

In the beginning, I mentioned that as a data engineer I created an analytic data mart for the data scientist team. Having become a data scientist myself, I was able to find new opportunities that increased the accuracy of the models, because I worked intensively with raw data in a previous position.

By presenting the results of one of our campaigns, I was able to show the models that generate higher conversion rates (as a percentage), and then one of the KPIs was measured. This demonstrated the value of the business performance model with which marketing can be associated.

3. Ensure model acceptance by showing its value to stakeholders

You will never be successful as a Data Scientist if stakeholders never apply your models to make business decisions. One way to ensure that the model is accepted is to find the pain point of the business and show how the model can help.

After talking to our sales team, I realized that two reps are working full-time, manually scanning the millions of users in the company database to identify single-license users who are more likely to switch to team licenses. The selection used a set of criteria, but the selection was time-consuming because reps looked at one user at a time. Using the model I developed, reps were able to select users with the highest likelihood of buying a team license and increase the likelihood of conversions in less time. This resulted in a more efficient use of time by improving conversion rates for KPIs that the sales team might relate to.

Several years passed, and I repeatedly developed the same models and felt that I no longer learned anything new. I decided to look for another position and ended up getting a data analyst position. The difference in responsibilities just couldn’t have been more significant compared to when I was a data scientist, even though I was back in marketing.

This was the first time I analyzed A / B experiments and found all ways an experiment can go wrong. As a data scientist, I did not work on A / B testing at all, because it was reserved for the experimental team. I have worked on a wide range of analytics studies that have been influenced by marketing, from increasing premium conversion rates to user engagement and churn prevention. I learned many different ways to view data and spent a lot of time compiling the results, presenting them to stakeholders and senior management. As a data scientist, I mostly worked on one type of model and rarely gave talks. Fast-forward a few years and move on to the skills I’ve learned to be a successful analyst.

Skills I Learned to Become a Successful Data Analyst


1. Learn to tell stories with data

Don’t look at KPIs in isolation. Tie them up, look at the business as a whole. This will allow you to identify areas that affect each other. Senior management looks at the business through a lens, and someone who demonstrates this skill gets noticed when it comes time to make a decision about a promotion.

2. Provide actionable ideas

Leave to business actionable idea to solve the problem. It is even better if you proactively propose a solution before it has already been said that you are dealing with the primary problem.

For example, if you said to marketing: “I’ve noticed that the number of website visitors has been decreasing every month”… This is a trend that they might have noticed on the dashboard and you didn’t come up with any valuable solution as an analyst because you only claimed observation.

Instead, study the data to find the cause and suggest a solution. The best example for marketing would be: “I have noticed that recently we have had a decrease in the number of visitors to our website. I found that organic search was the source of the problem, due to recent changes that caused our rankings to drop on Google.… This approach shows that you tracked the company’s KPIs, noticed a change, investigated the cause, and offered a solution to the problem.

3. Become a trusted advisor

You need to be the first person your stakeholders turn to for guidance or questions about the line of work you support. There is no shortcut because it takes time to demonstrate these abilities. The key is to consistently deliver high quality analysis with minimal errors. Any miscalculation will cost you credibility points, because the next time you submit an analysis, people might wonder: If you were wrong last time, can you be wrong this time too?… Always double check your work. It also doesn’t hurt to ask your manager or colleague to look at your numbers before submitting them if you have any doubts about your analysis.

4. Learn to communicate difficult results clearly

Again, there is no shortcut to learning effective communication. It takes practice and over time you will get better at it. The key is to identify the main points of what you want to do and recommend any actions that, as a result of your analysis, stakeholders can take to improve the business. The higher you are on the corporate ladder, the more important communication skills are. The transmission of complex results is an important skill that needs to be demonstrated. I have spent years learning the secrets of success as a data scientist and data analyst. People define success in different ways. To be characterized as “amazing” and “star” analyst is success in my eyes. Now that you know these secrets, I hope that your path will quickly lead you to success, however you define it.

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