As tools are evolving, data science job roles are maturing and becoming more mainstream in companies. The number of openings that companies have for data science roles are also on an all-time high. In a recent study conducted by Analytics India Magazine, we found out that there are more than 78,000 positions in data science that need to be filled.
With the growing lack of talent, companies are looking to hire large numbers of data scientists. Given the number of opportunities available, these are being expanded to professionals with a non-technical background as well. While there are many DS positions with a shortage of ideal candidates, it has made it quite possible for one candidate landing up with more than one job offer in hand for similar DS roles.
While data science and analytics job titles vary significantly in every company, it can be intimidating to decide from the job title alone whether what will be the responsibilities, seniority, position and other necessary deciding factors while picking the job. It is, therefore, necessary to ask questions about responsibilities, tools and methodologies used, type of data, how much time is spent on different aspects of the role (analysis vs. data management), and others. We have detailed on some of the key considerations below:
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Project And Skills Details: Data science is a skills-intensive job role and that’s why it is important to analyse the various domains and skills it makes use of or aligns with your interest. In data science and analytics, it is important to keep up with the latest tools and technologies, and therefore it can be a deciding factor if the job roles that you have been picked for offers that. Will they let you choose which tools you use, and do they encourage learning different methodologies? The project or the product has the highest deciding factor.
Infrastructure For Data Science Related Processing: This can also play a crucial role in deciding which job to pick. As a data scientist needs to leverage existing data sources and create new ones in order to create meaningful information and actionable insights, it largely depends on what kind of data infrastructure is there.
Growth Opportunities: What is the typical growth curve? How often are promotions handed out? What is the exposure to new tools and techniques in the current job role? These are some of the questions that can help in deciding whether there is a relevant growth opportunity a job offers. The one that weighs more should then be picked.
Training And Learning Assistance: When we say growth opportunities, training and learning assistance plays a key role. Since the skills for data science need to be constantly updated and adding more skills can only work in your favour in the future. Whether the current role conducts in-house training or allows other opportunities to train yourself and add skill can be a win-win situation. Training and learning assistance can be in the form of a short online course, certificate course or others.
Salary Is Crucial But It Should Not Overlook Skills: It cannot be denied that salary is the most important deciding factor while evaluating job roles. But it should not outweigh other aspects such as the opportunity to grow and learn. In skills-centric job roles such as data science and analytics, prioritising opportunities to learn and grow your skillset is crucial to stay on top of the game. If you have found your right pick in the job, but the salary is unsatisfactory, it could be negotiated in the final rounds, such as adding to bonus or others.
Other Benefits: What are the other perks offered in the job role such as flexible timing, flexible work schedule, food, work from home, travel allowances, health insurance, etc. can be important deciding factors.
Work Location: Needless to say, this will always be a crucial factor. A lot of companies have their headquarters in urban areas and the developed localities of the city, but if that is not the case, you know it instantly, which job role weighs more than the other! Whether it is closer to your residential place or not also plays a key role.
Does It Meet Your Future Goals And Plans: Before evaluating job offers, take the time to check with yourself about which direction you want your career to grow in. Do you want to expand your machine learning skill set? Or are you targeting a specific industry? What kind of data do you want to work with? Make sure that the roles you are applying to are in line with the direction you want your career to take. It should also be a good cultural fit. What is the ROI of your efforts? If your data science skills will be tried and tested in a way that you wish to? And more.
Every job offer may have its advantages and disadvantages, and after a careful evaluation of these points discussed above, it is ultimately the one which you feel strongly about that you could pick. It is important to analyse what you want from your job role, and willing to make adjustments in one of the few points mentioned above based on your personal pick. But it is still advisable that you have prepared your own checklist and spend a good amount of time analysing it before turning down the offer.