Startups are blooming like mushrooms and most of them fail to identify how data science can add business value at an early stage. However, I see how they start hiring for data-heavy roles earlier and understand better how data science can support their growth.
Is it then worth for a data science professional to work for an early-stage startup? I think so and I have put together my list of reasons.
Design and carry out better experiments
In lean organisations, it’s easier to promote best practices in experiment design. In bigger companies, experiments are limited by several factors including the inability to control the complete chain. Last but not least, It’s also more likely that you can take the experiment to the next step in a young venture.
Work across the organisation
In an early-stage startup, you will find more collaboration and communication between departments which will certainly improve the production of most data scientists. In exchange, the organisation will benefit from defining realistic and relevant data science project since the beginning.
Data science is an interdisciplinary field by definition and you can become the well-rounded data scientist that every company wants to hire.
Say goodbye to legacy code
Most data scientist spend a significant amount of time crafting lines of code. Particularly in companies where predictions should work in production environments and system availability close to 100%. Maintaining broken code is a common mistake that you can avoid if you start doing things right from the beginning. Late-stage startups can’t hardly afford rewriting a codebase from scratch.
Some “old” organisations find difficult to move away from machine learning algorithms that perform well, although they are nearly impossible to maintain.
Contribute to building the product
People seem to forget that data scientist can extract knowledge or insights from modest volumes of data in various forms. This can prevent product manager and owners from listening to the data when the results are not significant and leverage growth. In addition, a startup can build a more robust and scalable product if you benefit from using data to automate tasks since the beginning.
Data scientists in early-stage startups usually benefit from getting involved in the entire product line life cycle from strategic planning to operational activities.
Capture the data right
Companies usually identify the potential of accumulating data, so data losses are not critical. But later on they discover that they have to migrate away from data models and storage systems that are not good enough. Exploring your data and extracting insights from data helps the organisation to capture and store it the right way. You won’t have to say again “only if we had the right data…”.
What are your reasons to do it, or avoid it?