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Persistence Is The Key To Data Science Job Searches

Persistence Is The Key To Data Science Job Searches


Preparation for any career requires a lot of sweat and toil and in the case of data science, the preparation starts with searching for a job. A career in data science certainly makes for an attractive proposition for any aspiring engineer and the glut of courses in data analytics, AI, machine learning etc. point to the growing popularity of data as a career option.



Things can sometimes get very confusing for beginners sometimes, because constantly evolving job profiles, updated roles and changing tech can be overwhelming.

In this article, we will list down the steps one can take to approach the job hunt in a methodical and efficient manner:

About Your Future Employer

It is smart to identify employers first. Build up a list of six or seven target organisations and spend a lot of time learning more about them. You can try to get closer to them through mutual connections, social media and job boards and kind of set a bit of groundwork for the future.

Examine Your Priorities

Before pitching yourself as the perfect candidate for a data scientist role, think about your priorities. Do you think you are a good fit for the role? Is this job title relevant to you? Do your main skills fit their requirements? Do you have have a portfolio of achievements? Does this employer appeal to you and why? It is always wiser to think about these questions through before going on a face-to-face meeting with important decision makers.

Become An Obvious Fit For The Company With Your Resume

The art of writing a concise and to-the-point resume is a talent that many of us haven’t mastered yet. Many a time, our key achievements are lost in long essays and in many cases, our recruiters are least impressed.

Every aspiring data scientist’s skill set must include:

  1. Statistical and mathematical skills
  2. Strong programming and computing dexterity
  3. List of projects
  4. Skills to handle unstructured data
  5. Strong communication skills
  6. Academic qualifications

Strong Portfolio

There is a lot more competition at the entry and intermediate levels since professionals from different career paths want to break into this field. That’s why a strong data science portfolio which is a mix of

  • Machine learning projects
  • Data Visualisation
  • Exploratory data analysis projects

…helps in demonstrating the skills and qualities needed for that particular role.

See Also

Method Before MOOCs

Most companies aspire to hire Facebook or Microsoft-level talent. However, graduates who bridged the learning gap with MOOCs or full-time undergraduate programmes usually approach the subject from a tools-first perspective. This is unlike the university-backed courses or real industry experience, where one applies techniques and methodologies in a way that can address problems in a clear way which can eventually inform and influence the decision-making process. In short, the right candidate is expected to have relevant statistical nuances to understand the data.

Go Beyond Titles

There are multiple positions opening across the industry and one can also look for titles or positions like these instead of just “Data Scientist”:

  • Research Analyst
  • Business Analyst
  • Data Architect
  • Data Engineer
  • Algorithm Bias Auditor

Go The Non-Traditional Way

While companies have been hiring the traditional way through job portals, that typically includes getting applications, pre-screening, technical test, personal interview and selection, companies are now opting for other non-traditional ways to hire data scientists. Some of the avenues are:

Also see:


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