As a new data science graduate, it can be painful trying to find a job in this uber-competitive market.
300 applications later and you’re still fighting against 500 other applicants for the same entry-level position. However, despite what some may say, Consulting as a new data scientist is completely possible. Why fight hoards of applicants when you can create your own job?
Data scientists, even those who just graduated, have a unique skill set that can be used to help clients in a variety of scenarios without having any traditional work experience for a company. The trick is to have the right amount of patience, tenacity, and resilience to set up your consulting business. Working for yourself can be scary, but data scientists are lucky to have in-demand skills that can be used to provide one-time services (or even repeat work) to customers looking to get more out of their data.
This guide will help you get the ball rolling toward developing your own Data Science consulting job in 7 minutes or less.
How to pick your specialty
Despite the general requirement for data scientists to be "jacks of all trades", working as a consultant means that you’ll want to pick a specialty to avoid going crazy with stress.
For example, you may feel most comfortable working with just a few simple tools to provide analyses that don’t require Machine Learning or artificial intelligence. This type of specialty would focus on data analyst tasks, such as cleaning data using Excel, doing simple analyses using Python, and developing visualizations using Tableau or another similar tool.
Alternatively, you may thrive in the mathematical jungle of creating predictive machine learning models that involve artificial intelligence to help clients predict the futures of their businesses.
The key is to know what you’re good at and focus on it. Going out on your own as a consultant is scary enough – ensure that you’re going to be marketing and using skills that you’re comfortable with. Having confidence that you can successfully produce results using your tools and skills of choice goes a long way to becoming a successful consultant.
Additionally, do some market research to see where your niche could lay. While they say that data scientists should all be generalists in the beginning, I believe that consultants should focus on specializing themselves in niches that complement their skills and their alternative knowledge. For example, I would focus on becoming a data science consultant who specializes in helping companies solve their environmental problems – this would combine my specialized skills (data science) with my alternative knowledge and educational background in environmental science. Companies love working with consultants who have first-hand experience in their sector, so it can’t hurt to play to your strengths, past employment, education, or interest background.
How to market your skills
The most simple checklist possible to begin marketing your skills is to: (1) build a portfolio; (2) begin networking; and (3) attend industry events. These three to-dos are the cornerstone for marketing your skills as a new data science consultant.
Building your portfolio will help potential clients better understand the type of services you can provide them. Your portfolio should be full of projects that are relevant to the services you’re planning on providing – predicting the stock market isn’t a great indicator of your skills when you plan on providing customer retention data services.
How to Create a Professional Portfolio on GitHub That Will Help Land Your First Job in Data Science
Networking is your next task and will involve reaching out to everyone you know: past classmates, instructors, friends, and family. Many suggest cold-calling every business listed in your local yellow pages, but I find that connecting with people you know can lead to referrals that are more personal. When someone you know reaches out to a potential client on your behalf, a personal connection is instantly formed between you and that potential client. You can then leverage this personal connection to develop a paying client. However, you must make sure that each client you gain this way is given the gold standard of your services – your personal contact stuck their neck out for you so you must return the favor by being as great for this client as they said you would be. These are the clients that you should go the extra mile for, by building interactive dashboards, developing custom reports, and ensuring an extra level of customer service and attention.
Finally, attending industry events is a great way to network with people you don’t know but would like to have as clients. All industries are needing data services which means that you don’t have to limit yourself to data science-specific networking events. If anything, you want to stand out in a complementary industry instead of being just one more consultant looking for a client. For example, head to industry events for your target industry and be the one data science consultant there who can create connections with potential clients.
One of the best pieces of advice I was given for having success at networking or industry events is to have a QR code on your business card that can take people directly to your portfolio or website. This is a great way to speed the connection process and helps direct potential customers to what’s important right from the beginning.
How to find clients
After you’ve figured out your data science specialty and marketed yourself as a new consultant, you’ll need to learn about the different ways you can find clients. Luckily for data scientists, there are several options.
The first way to find clients is to do pro bono work for small businesses in your area to develop some projects and gain credibility. Pro bono work is a great way to put some experience on your resume and build a portfolio that shows projects that have created real impact for a client. You can get these clients by reaching out to small businesses and offering customized services to help them solve their data problems. These clients will also be more likely to provide great references to paying clients due to you providing high-quality work for free.
The next way to get clients is to become the go-to data science consultant for clients. By amassing a small client pool where you’re the go-to person, you create the perfect environment for word-of-mouth to spread your credentials, skills, and services to other similar potential clients. This is also the best way to retain clients – by sticking around to do additional work (even part-time) for clients you like and that like you, you create valuable connections that will provide work for years to come. Additionally, any subsequent work that you do for these clients will be easy because you’ll be familiar with their data structures, systems, business beliefs, and goals.
The final way to begin getting clients as a new data science consultant is to bid for micro-projects with companies who are outsourcing work. Sometimes, big companies will bid for RFPs and then outsource some of the activities that come with the contract. These little jobs may only handle a couple of small aspects of the overall data project but are a great way to get your foot in the door and some experience on your resume.
How to price your services
The biggest question when becoming a data science consultant is how to price your services. To keep this article short and sweet, the only advice I can give that is general enough for all regions and new consultants is this: don’t undersell yourself.
Sure, it can be tempting to say that you’ll work for anything. But how do you demonstrate your quality and ability by working for peanuts?
When clients are hiring consultants, they don’t necessarily want the cheapest one. With data science consulting, you get what you pay for. This means that most clients are looking for someone who provides excellent value for their cost without being smoke and mirrors on the high end or just plain disappointing on the low end.
Pricing can be tricky given that there are several different ways for you to bill your services. For example, you could bill hourly, on a monthly retainer, through set product pricing, through value-based fees, or through a pay-for-results model. Hourly billing is self-explanatory, as is the monthly retainer model where you work for a flat fee each month. Alternatively, you could sell your services via a suite of products that you sell at a flat fee or with varying prices for different tiers of products (think interactive dashboards, reports, advanced levels of customization, etc). Value-based pricing is set through an agreement between you and your client where they decide how much to pay you based on the value of your work for the company. The final way you could price your services is through a pay-for-results model where you don’t get paid if your services don’t improve the client’s business.
As an entry-level) data science consultant, you could look at charging between $200–250 per hour and begin charging upwards of $300 per hour as you gain more experience. This will vary depending on the industry that you provide your unique services to, with a likely ability to increase your rate for clients in the oil and gas sector and a likely requirement to decrease your rate for small businesses.
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