Someone can call himself or herself an excellent data scientist when one has mastered the programming languages, data structures, visualisations, varied algorithms, complex analysis are the benchmarks for a good data scientist, there are others who have all the necessary skills but still fall for the wrong type of data science Job.
In this article, we will list down 5 things to know before accepting your dream data science job offer or not.
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Major Skills To Gauge Your Eligibility to Land A Data Science Job in 2019
- You know how to tell a story with your data.
- You understand design principles to better understand how to visualize data.
- You have an in-depth foundation in statistics which helps you understand large datasets.
- You are part of a data science community or group where you brainstorm and bounce ideas off.
- You focus and apply varied concepts and programming languages in practice.
Questions To Ask Before Accepting The Job (Self Evaluation)
- Is this role can help you carve out an area of expertise for yourself?
- How well does the team function; how well do the members get along?
- Are the responsibilities distributed equally among team members in an unbiased way?
- Does the company provide opportunities to learn from senior developers and work under their mentorship/tutelage?
- What kind of training opportunities or skill enhancement workshops are currently conducted in the company?
- Are there any inter-department collaborations that are encouraged?
- What types of data science projects you’ll be given to work on? How do the team manage its active and latent projects?
- What are the major successes of the team and due to the failures also?
- What is the overall vision of the team?
- Does the company allocate time to focus on finding solutions rather than on tools?
Top 4 Reasons To Not Join A Data Science Team –
- Inefficient data architecture & no set defined objectives: There are set practices in data science industry and that creates data science specific projects actively. If they don’t have in-house experience or the data architecture to support a data science team. But if a company does not provide a particular data science environment with set goals and a preset time frame. Then that’s a warning signal that the company lacks a strong data science practice.
- Non-inclusion of data engineers or machine learning engineers: Non-inclusion of data engineers and ML engineers means the only work that you will be doing is building data pipelines and writing SQL queries or playing with data around. due to non-availability of ML experts, you have to put completed data science projects into production which is an unprofessional thing to do.
- No defined holistic vision for the team future: If there is no defined comprehensive vision for the team in terms of projects and personal growth, both of which are important then it’s other hands down to decline the job offer as both the company should have set long-term goals which should correlate with yours before accepting any job offer.
- The focus is only on tools and not over problems: If a data science team just focuses on tools over problems, then this may signal that they are more concerned with impractical things. Teams that focus on problems over tools will make sure that data science projects are providing value to company owners.
- As a data scientist, there are lots of things one can optimise when looking for a new job, for there suits one or the company has data science projects one will be excited about.
- If a data science team has good answers to many of the questions above, they are probably doing well and are worth joining.
- By self-evaluating based on the questions that we have posted one can easily land their dream data science and can have a fulfilling career as a data scientist.
- Your long-term growth is just as important as what you work on in your day-to-day.
- The data science and big data industry are constantly growing in the coming years and the demand will increase for the data scientist. jobs too, but staying away from the herd and choosing the right kind of job is important.