On-demand Data Science

On-Demand Data Science: A Case for freelancing your analytics

Sajeev Popat

Article published: January 21, 2021

We’ve all heard it before: it is “mission critical” to “leverage” data. Well, no kidding - the question is how? Every analytics company under the sun has been telling us this “leverage data now” message for years. Yet, for some reason, despite the number of tools and platforms flooding the market, the ability to analyze and utilize data is not getting easier. Good data analysis still requires a skilled professional who can apply cutting edge techniques to help your company “leverage” your data effectively. But how do you find this person?

With the rise of the gig economy and the advent of freelancing, there’s a way to source people to solve analytical problems. COVID-19 has only accelerated the importance and acceptance of this analytics freelancing practice. Here’s why you should consider using on-demand data science and analytics freelancers to “leverage your data”.

1. It is really expensive to hire a full-time data scientist.

Expensive Data Science

Today, it’s really expensive to hire a data scientist. The average annual salary is more than $113,000. Whether you are implementing machine learning into your product or building a smarter lead scoring model for your sales team, you can’t afford to get the data science hire wrong. You could invest time, energy, and money in finding a full-time hire, but meanwhile those high priority projects are collecting dust leaving your company vulnerable to competitors who have expanded their own analytical capabilities. Using a freelance data scientist is an inexpensive way to address immediate data-driven challenges facing your business without spending six figures and spending several months to find a full-time hire.

2. For discrete challenges, you do not need a full-time hire

Discrete Challenges

Not all problems take years to solve. In many instances, you only need dedicated attention on a problem for a couple weeks to generate value. Pricing analysis is an excellent example of an acute analytics need. As companies add new products or react to competitors and market conditions, it is important to reexamine pricing. However, pricing analysis should be a discrete task not an ongoing project. Having someone focused on the project for a short period of time is the best way to ensure your company has the right pricing for your products. In circumstances like this, hiring a short-term data scientist is a better option than diverting internal resources away from other priorities or hiring a dedicated full-time resource.

3. Capacity constraints should not limit your business

Capacity Constraints

No matter how well staffed an organization is, there’s always a backlog of projects that no one has the time to work on. This is particularly true of analytics and data science teams. Some projects don’t appear important enough in the short-run but could yield a lot if someone just had the time to work on them. For example, imagine a software business that collects detailed usage data from their customers. Running experimental analysis to find insights and patterns within the data could lead to new product offerings. However, because customers are demanding certain features or the marketing team needs that lead scoring model to hit revenue goals, these projects remain stuck in backlog purgatory.

By using on-demand data scientists, organizations have the flexibility to take on backlog projects that, for a comparatively lower cost and commitment, could generate high returns. This way, a team can effectively scale up and down their capacity and productivity opening the possibility for new growth and opportunity.

4. Test-drive before you buy to ensure the best outcomes

Hiring Interview

Hiring in general is a risky and painful process. For finding data scientists, not only do you have to shell out six figures but you also make sure there’s a strong ongoing use-case for that hire. While some companies may want data science, they may not be ready for it. Using an on-demand resource de-risks that process and enables companies to test-drive the use-case before they “buy”. For example, a VP of Marketing of a B2C business may recognize the need to use the vast amounts of data collected to do better campaign attribution, personalization, A/B testing, or lead scoring. But as is common with analytics, it’s hard to see the benefits up front. Therefore, making that expensive investment into a full-time hire is hard. However, the VP of Marketing could work with an on-demand data scientist on a lead scoring model first to quantify how useful a dedicated resource would be. After building and deploying a model over the course of a couple of months, the VP now would have a proof-of-concept for data science to share with the other members of the executive team. Without the cost or pressure to exceed expectations, she would then have buy-in and support to invest in building an analytics function.

So for companies struggling to “leverage” their data, look to flexible resources to get up and running quickly. On-demand data science is an easy way for companies to get value from their data and get access to the top analytical talent without breaking the bank.