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Best Practices To Increase Data Science Job Satisfaction

Best Practices To Increase Data Science Job Satisfaction


Data is the new oil for businesses, and therefore data science has emerged to be arguably one of the most preferred jobs of this decade. Data science is a competitive weapon and businesses of all sizes have been scrambling to find top talents. Therefore, the demand for data scientists is way higher than its supply. The data science sector is flourishing to such an extent that our earlier jobs study revealed that there are currently more than 97,000 job openings for analytics and data science in India right now.

However, hiring data scientists isn’t the biggest hurdle; it’s, rather, keeping these individuals engaged in the job and retain them for the future. According to the study, only 13% of the respondents said that they were happy at their current workplace. Nowadays, it is believed that organisations’ biggest data science challenge isn’t about the new technology or data itself; instead, it is the people and ways to retain them. In this article, we are going to discuss strategies to retain your data science talent in a competitive market and the ways to develop data science job satisfaction.

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Enhancing Work Practices

Creating employee satisfaction includes more than just free meals, office trips, and TT tables. Organisations need to keep their data scientists engaged and satisfied with their current job roles. It is very important for a data scientist to feel that the practices at work are just and fair. The key is to create an overall vision for the work they are doing and help them understand their value in the company’s mission. 

Additionally, it is also important for data scientists to have a clear connection with the rest of the business to create transparency across the enterprise. A popular method at 33%, according to the study, is involving data scientists in decision making. Many data scientists even voted to see more hands-on meetings and more feedback from their peers. The industry is booming with something new to explore every day. Therefore, organisations should aim to create a power-packed learning environment, which will enhance the personal and professional growth of data scientists.

Improving On The Go With Upskilling

In today’s world of data, if an individual isn’t able to keep pace with new technologies and capabilities, they become redundant. Therefore, many organisations take initiatives in upskilling their employees and improving their skills during the work tenure. There could be immense scope for learning and improving, be it technology related, business domain related or soft skills related. Even in our survey, we found out that all of the data scientists wish to be upskilled in all ways possible. 

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To keep talented data scientists interested in their work, organisations should take active efforts to increase engagement — from hackathons to brainstorming sessions. Training, workshops, conferences, events, and mentorship and innovation contests are some other initiatives that organisations can take to improve their employability skills in the field. 28% of the data scientists also said that they would appreciate it if their employers tied up with noted institutes for customised certification programmes.

Internal cross-functional learning sessions along with external training sessions are also helpful for data scientists to learn new techniques on their job. Serving the right tools is another aspect that is extremely crucial for the organisation to take care of. Without the right tools, data-minded professionals may not be fully equipped to get the desired results from your data.

Providing Growth Opportunities For Career Advancement

Innovation has been the core of data science, and therefore data scientists always want to be a part of it and drive it. Data scientists are also driven by focus, and therefore they value the flexibility of time and the ability to dig deep into subjects. It is also crucial for organisations to be data literate and be thoughtful about the work given to their data scientists. Because if the data science team is dealing with every aspect of data and serves as the de facto interface, it will be very difficult for them to come up with any desired solution. 

According to our survey, 15% of the respondents said that they felt trapped in their current employment due to the lack of career advancement opportunities. A data scientist should always be given high-value projects along with the tools, leeway and a perfect environment, which will keep them motivated and engaged, which, in turn, will enable them to achieve big things. The poor use of their talent can put the future of the business at risk. It is believed that organisations shouldn’t underestimate the importance of retaining data scientists as the role of AI, and ML is only going to increase as businesses look to gain a competitive edge.

Getting Data Strategy Right

To hire and retain data scientists, organisations need to know the basics of their data and how to handle it. A data scientist would always like to join a company that has a clear mission and strategy of processing data and capitalising on the same. Having a robust data strategy — in other words, what is your business trying to achieve — will provide a clear vision to data scientists about the problem and what is expected out of them. 

No data scientists would like to surf through terabytes of data only to find relevance for the business. In fact, stress has become one of the main reasons for data scientists to switch companies. In the aforementioned study, we found out 13% of the data scientists think that their work in their previous company was stressful and therefore they switched.

Having a data strategy will ensure that the data is managed well and can be used as an asset by the data science team. It will provide the team with a standard set of goals and objectives across projects to ensure that the data is used both effectively and efficiently. A defined data strategy will establish standard practices to manage and share data across the enterprise, which can be used to make informed decisions, understand customer trends, provide smarter services, improve internal operations, and create additional revenue.

Cross-Training Data Scientists

Along with strategising the data and communicating the role of data scientists, organisations also need to describe the purpose of data in their businesses. Data scientists can process and analyse the data gathered in a better way if they are aware of why the data is being gathered, and what are the aspects of its usage in the business. 

Organisations need to create a strong relationship between their data scientists and business executives by cross-training them with other business aspects. Cross-training data scientists with different business roles can give them a better clarity of the organisation and also help them grow in their career path towards more C-level positions — it works as a motivator for the talent. 

A few companies also take a step further and identify insiders who have analytical skillsets and train them in data science. Having such liberty will help data scientists to correctly fit data analytics into a narrative context for the whole company. It will also help them see the financial implication of their work.

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