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Is there a place for data science generalists in the job market?
With some introspection these last few months, I found that no one field (e.g, analytics, natural language processing, experimentation, etc) in the data science space really draws me (though all are very interesting in their own way). With that being said, is it frowned upon in the job market to be more of a generalist data scientist than a specialist data scientist?
In the past, my academic journey has allowed me to earn degrees in history, anthropology, mathematics, computer science, statistics, economics, political science. I suppose this conundrum I'm having in the data science space closely mirrors my experience back when I was an undergraduate.
Perhaps a company or business may derive more value from someone who is more specialized in their subfield than someone who is fluent in most modalities but not particularly an expert in any one subfield.
Though I would have to also add that domain knowledge and understanding of business context are important skills to have regardless if you're a generalist or specialist data scientist based on my what I've observed.
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Dhairya’s Answer
Hi Jimmy,
Great question. Yes, you can absolutely be a generalist. Often people's careers follow a T shape, where you start with a broad and foundational set of skills and then narrow down a speciality. But it's the not the only way to build a career.
You should look into Data Science consulting and perhaps more broadly management consulting. As a consultant you end up working on many different scoped projects. Having a generalist set of skills here is valuable as each consulting contract can be in a different business verticals (e.g. healthcare, finance, education, etc) and require different set data science methods. Management consulting has evolved into quantitive strategic analysis and may also be appealing to you. Management consulting firms (e.g. Mckinsey, BCG) will hire generalist data scientist to work more technical consulting gigs.
Great question. Yes, you can absolutely be a generalist. Often people's careers follow a T shape, where you start with a broad and foundational set of skills and then narrow down a speciality. But it's the not the only way to build a career.
You should look into Data Science consulting and perhaps more broadly management consulting. As a consultant you end up working on many different scoped projects. Having a generalist set of skills here is valuable as each consulting contract can be in a different business verticals (e.g. healthcare, finance, education, etc) and require different set data science methods. Management consulting has evolved into quantitive strategic analysis and may also be appealing to you. Management consulting firms (e.g. Mckinsey, BCG) will hire generalist data scientist to work more technical consulting gigs.
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Carrie’s Answer
Data science is a high demand job. If you have the basics I think it is a good start, however, I do think if you specialized there would be more opportunities for you. If there are fields that you have interest in for example medicine or criminology, you could focus on learning some of these. If you focus on specific programs that those organizations use to read and interpret their data in those fields this is a leg up. I think there is more opportunity for moving up if you specialize.
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Holly’s Answer
Hi Jimmy - I agree with Carrie. Data scientists are in high demand. Almost every industry today can derive value from these skillsets. I would recommend spending time thinking about what problems excite you and that you would like to solve. Then look into data science opportunities in those areas. I have not done any analysis on this topic, but there may be some industries where there is more demand than others, so that could also be a factor you evaluate. Good luck!
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Rahul’s Answer
Is there a place for generalists with domain knowledge that relates to the task at hand? Yes, absolutely yes!
You can definitely come in as a generalist and then specialize in one or more techniques, depending on the scope and need for applying those in your place of work - a lot of professionals I've come across, were able to pick up skills and the required know how while working on projects.
So, the sky's your limit :)
You can definitely come in as a generalist and then specialize in one or more techniques, depending on the scope and need for applying those in your place of work - a lot of professionals I've come across, were able to pick up skills and the required know how while working on projects.
So, the sky's your limit :)
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Gillian’s Answer
Hi Jimmy!
Just going to mimic everyone’s opening answer and say that, you will have absolutely no problem finding a vast quantity of job listings for generic data science jobs; they’re usually listed as “Data Scientist”. Usually those looking for specialists will include the type of specialization in the job title or description.
When I first started in data science I felt overwhelmed with the amount it specializations to choose from and was in a very similar predicament to you. Through my two generic data science jobs that I have had so far, I am starting to discover what niches of data science I prefer and which I don’t. This is a process that takes time to allow you to explore and get good at these subcategories, so definitely explore generic data science at a jumping off point.
If you’re going to go back to school for this, I highly recommend working an internship in it at the same time, as it will greatly increase your odds of being hired directly after (and sometimes slightly before) graduation.
Research and practice generic data science principles in your free time to make sure that this is something that you are truly passionate about.
Research ‘data scientist’ job offerings either through linkedIn or Indeed and see how many of the skills you qualify for and which you need to brush up on.
Just going to mimic everyone’s opening answer and say that, you will have absolutely no problem finding a vast quantity of job listings for generic data science jobs; they’re usually listed as “Data Scientist”. Usually those looking for specialists will include the type of specialization in the job title or description.
When I first started in data science I felt overwhelmed with the amount it specializations to choose from and was in a very similar predicament to you. Through my two generic data science jobs that I have had so far, I am starting to discover what niches of data science I prefer and which I don’t. This is a process that takes time to allow you to explore and get good at these subcategories, so definitely explore generic data science at a jumping off point.
If you’re going to go back to school for this, I highly recommend working an internship in it at the same time, as it will greatly increase your odds of being hired directly after (and sometimes slightly before) graduation.
Gillian recommends the following next steps:
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
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James Constantine’s Answer
Dear Jimmy,
Absolutely, the job market holds a substantial space for data science generalists. While some firms may lean towards specialists in distinct data science areas, a multitude of organizations highly appreciate the versatility of well-rounded data scientists who possess a comprehensive skill set, enabling them to tackle diverse projects.
As a generalist, your varied academic background would be a unique asset. Your knowledge in fields like history, anthropology, mathematics, computer science, statistics, economics, and political science equips you with a robust foundation in both the practical and theoretical facets of data science. This wide-ranging knowledge allows you to tackle problems from various perspectives and communicate effectively with stakeholders across departments.
Furthermore, data science projects often necessitate collaboration among specialists from different areas. A generalist can serve as a vital link between these specialists, ensuring a unified approach towards a common goal. They can also recognize when a specialist's expertise is required for a specific task and orchestrate their contributions.
However, it's crucial to remember the importance of having a firm grasp of the fundamental data science skills, such as statistical analysis, machine learning, and programming. Specializing in one or more areas can enhance your marketability and make you more valuable to potential employers. But being a generalist doesn't exclude having expertise in certain areas; it merely implies a wider skill set applicable to a range of projects.
Beyond technical skills, a deep understanding of the business context and domain knowledge are essential for any data scientist, be it a generalist or a specialist. A profound understanding of your working industry or field can guide you to ask the right questions, pinpoint crucial issues, and effectively convey insights to stakeholders.
In conclusion, despite a possible bias towards specialists in certain industries or companies, generalists undoubtedly have a significant place in the data science job market. Employers appreciate data scientists who can handle various projects and collaborate with specialists from different areas. As long as you maintain a solid foundation in data science's fundamental skills and possess domain knowledge relevant to your working field or industry, you can thrive as a data science generalist.
May God bless you!
James Constantine.
Absolutely, the job market holds a substantial space for data science generalists. While some firms may lean towards specialists in distinct data science areas, a multitude of organizations highly appreciate the versatility of well-rounded data scientists who possess a comprehensive skill set, enabling them to tackle diverse projects.
As a generalist, your varied academic background would be a unique asset. Your knowledge in fields like history, anthropology, mathematics, computer science, statistics, economics, and political science equips you with a robust foundation in both the practical and theoretical facets of data science. This wide-ranging knowledge allows you to tackle problems from various perspectives and communicate effectively with stakeholders across departments.
Furthermore, data science projects often necessitate collaboration among specialists from different areas. A generalist can serve as a vital link between these specialists, ensuring a unified approach towards a common goal. They can also recognize when a specialist's expertise is required for a specific task and orchestrate their contributions.
However, it's crucial to remember the importance of having a firm grasp of the fundamental data science skills, such as statistical analysis, machine learning, and programming. Specializing in one or more areas can enhance your marketability and make you more valuable to potential employers. But being a generalist doesn't exclude having expertise in certain areas; it merely implies a wider skill set applicable to a range of projects.
Beyond technical skills, a deep understanding of the business context and domain knowledge are essential for any data scientist, be it a generalist or a specialist. A profound understanding of your working industry or field can guide you to ask the right questions, pinpoint crucial issues, and effectively convey insights to stakeholders.
In conclusion, despite a possible bias towards specialists in certain industries or companies, generalists undoubtedly have a significant place in the data science job market. Employers appreciate data scientists who can handle various projects and collaborate with specialists from different areas. As long as you maintain a solid foundation in data science's fundamental skills and possess domain knowledge relevant to your working field or industry, you can thrive as a data science generalist.
May God bless you!
James Constantine.