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Quick Start Guide On Landing A Data Science Job For Senior Professionals

Quick Start Guide On Landing A Data Science Job For Senior Professionals

  • We spoke to Ram Sheshadri, Google Machine Learning Consultant and Mathangi Sri, VP, Data Science, Gojek, to get insights on how to go about landing a data science job.

The demand for data science professionals is at an all-time high. Currently, around 138K open positions exist in the field of analytics and data science in India. Additionally, 97 percent of the analytics jobs advertised in India are full-time, as per the Analytics and Data Science Jobs in India 2021 study

Data science is still a new field. Most modern organisations are exploring the potential of machine learning and artificial intelligence. 

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Below, we have listed points to keep in mind while searching for a data science job, especially for senior professionals. We also spoke to Ram Sheshadri, Google Machine Learning Consultant and Mathangi Sri, VP, Data Science, gojek, to get insights on how to go about landing a data science job.

Setting goals

It’s important to have a clear vision. Are you excited to delve into cutting-edge research work? Are you looking to drive business outcomes through data science models? You need to weigh your options, analyse your strength and weakness, and set goals before you pivot.

“If you are a senior professional, ask yourself what is your ideal operating model? Do you want to create an impact in a greenfield area? (for example, building credit scores for new to credit customers in markets like India) or do you want to build on top of existing AI solutions in a very data-rich organisation. This could also determine whether you are going to build teams ground up or inherit a large team and take their charter further,” said Mathangi.

Different organisations are at various stages of the data science maturity curve (some are beginning to explore data science use cases, some have a dedicated data science team, while others are at a level of integrating data science into the wider business workflows). It’s crucial to know what machine learning adoption levels will best suit someone’s profile. Further, work culture matters. You can get an idea about a company’s work culture through websites like Glassdoor.

Practice

Honing your data science skills either by solving problems on Kaggle or by creating one’s own data sets, gives you an edge. Working on our coding skills, understanding the ins and outs of ML algorithms and participating in hackathons keep you sharp.

“Teach while you wait. Or better still, take a contract job teaching while you search for a bigger role or a better position. There is nothing like teaching to keep your skills fresh while at the same time getting in front of potential employers who might be sending employees to your classes. I found this to be the best use of your time,” said Ram. 

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If teaching is not your cup of tea, you can sign up for Cloud certifications to boost your resume.

Networking

Being active on LinkedIn gives you visibility. “Just posting congratulations messages or adding a comment or two with your own experience from various industries is a great way to let potential recruiters know about your deep expertise,” said Ram.

Further, creating informative and valuable posts, especially with code snippets, is a great way to get the attention of potential employers. 

Networking and building relationships with the data science community bring you up to speed on the latest advancements in the field. In person meetings can go a long way in landing the right job. Last but not least, do not hesitate to get in touch with former colleagues and bosses for referrals.

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