Why Startups Need Data Scientists Now more than Ever
It is a given that the need for data scientists is ubiquitous. In the early days, one may think data scientists were required in only tech firms. But right now, data scientists have permeated into every stratum of the business world, including fields such as law, agriculture, education, and healthcare. And so is the need for a data science online course.
Both big and small companies are constantly looking for ways to leverage the power that untapped data holds. With the massive use of social media and the internet, the data generated has skyrocketed. Business owners have quickly realized that these untapped data hold the keys and potentials to their breakthrough.
For startups, the Data Scientist would need to develop a model from scratch, train the model with existential data, deploy it to production and evaluate its performance. In big companies, they may not need to build models from scratch since they ride upon years of experience and big data.
A data scientist in a startup would need to build models and evaluate them based on key business metrics. For instance, they can create predictive models that envisage a customer’s behavior, test the effect of new product changes, find the best price point for a product, and so on.
What Data Scientist in a Startup should constantly think about
The secret to getting the best from your target audience is to have a personalized approach to every single one of them. You need to understand that person A is quite different from person B but can have similar attributes to person C. Making these extrapolations manually is a piece of cake if you have a handful of customers. But it becomes impossible if you have thousands of customers in their thousands. Data science and machine learning help data scientists achieve personalization at scale. Personalization in simple terms means predicting what a customer wants and putting it in front of them and watching them make the purchase.
Without explicitly telling the computer, the Data Scientist or a gaming company can recognize that teenagers who buy Call of Duty games always buy FIFA and adults mostly buy only Asphalts. With this, he can make recommendations based on a customer’s previous purchasing decision.
Data scientists in a startup must go beyond building descriptive models to predictive models. At that point, the data scientist can treat every single customer as a standalone entity and predict what they would like without hassle.
How Data Scientists operates in Startups
In a startup, a Data Scientist should do three basic things.
- Create predictive models
As mentioned earlier, startups must ensure that they are big on personalization. This involves extracting insights from raw data and passing them into a model that can make future predictions. It is largely used in understanding customer behavior and making data-driven decisions to increase product adoption and sustainability. You can learn more about how to build a predictive model as a Data Scientist by enrolling in data science training with python.
- Building data-driven products
Data scientists should help startups build data-driven products. As a data scientist, you should not stop at creating predictive models but also deploying it in production and seeing how it performs under real-life situations. This guides the data scientist on the right kind of product that customers crave for. The model deployment also helps to find loopholes in the data. By integrating the data specification in a physical product, potential issues with the product are found and rooted out.
- Automate tasks.
As a data scientist, you must attempt to yank off repetitive tasks. Of course, this can be done by building machine learning models that do these tasks and boost the productivity of the team. When it comes to tasks automation, there is always fear as to whether data science will come and wipe jobs away. In the real sense, this may not be correct. I’d explain.
Will Data Science Take Our Jobs away from us?
First of all, the primary aim of a startup is to scale well and effortlessly and not to stockpile capital in a bank account. By using data science to improve team productivity, the startup tends to perform better overall. Without a doubt, this may be at the expense of some job positions. But the effectiveness of the team would ultimately lead to the expansion of the company. This by extension means, more individuals would be employed.
Thus, looking at it holistically, Data Science is not coming to take our jobs from us but to add to it. It is more like a win-win situation where the startup makes more money, while the working class has more job opportunities.
Wrapping up
To wrap up, you have realized that startups more than any other business need data scientists intensely. They are more like a backbone that ensures the sustainability of products and business progress. Data scientists majorly work by creating predictive models to enhance personalization, creating data-driven products, and automating repetitive tasks. If you want to learn more about how data science works, you should consider getting into a data science online training.
