Should You Hire Data Science Freelancers, or an Outsourced Data Scientist team?

Cost and Budget

Unlike many other sectors where a freelancer may be the best option for a smaller budget, when it comes to data scientists, you may want to think again. Small-scale one-off ML systems will probably be best resolved through third-party vendors. These vendors will have prestructured workflows and be able to maintain your product functioning reliably.

However if the ML system is a core component of your project or business model, an in-house data scientist will be indispensable, with freelance work being done as support if it is not possible to hire a whole team.

Project Specifications

Many startups and small-scale companies will be able to do fine without a data scientist in the early stages. Data-driven companies can however have an advantageous edge at attracting customers and every founder should contemplate how a data-driven approach could potentially optimize their strategy.

Considering your company’s product and strategy will define if you will need a data scientist immediately, after a while (more likely), once your company is firmly established, or never. Whichever is the case, it will be important to set up a data infrastructure before undertaking a data science project.

This part of your project considerations will also help you determine what part of the process will require greater attention so you can choose your prospects for data scientists accordingly. You may need to specialize the project with software engineers focused on instrumental logging sensors for data collection; data engineers to clean and build the pipelines to store and transform that data; a data science analytics department for optimizing and labeling the data, and research scientists and ML software engineers for deploying the AI; or the project might not require so much specialization and may be able to be handled by just a few people.

Project Risk

The more data-driven your company is, the greater the risk will be when investing in a robust, future-proof data science solution. A correctly implemented data science solution can capture the target audience more efficiently and thereby drive profits as a result. However, it is important to mitigate these risks by building up the system sustainably.

Working with data, by default, comes with trial and error. So as a rule-of-thumb, the minimum timeframe to work with a client on a complex project is a minimum of 6 months to see any type of results. The larger and more risk-averse the project, the more useful extensive expertise, specialisation, and documentation is required.

The more modest the potential impacts of the project, the more restrained the budget should be to avoid a potential leak of resources. Maintaining the project spending proportional to results with proper project management and maintenance will allow for sustainable growth of your data science.

Should You Choose a Data Science Freelancer or Remote Data Science Team?

Freelancer

A great advantage of hiring a freelance data scientist is that it is a much more agile process than onboarding a whole team. If you are at the start of your project you will be able to get started on things like setting up your data infrastructure before you have to commit to a more long-term arrangement with a team that will provide you a product from that data and continuously gives the system maintenance.

This last point goes hand in hand with being able to start setting up a data pipeline with a minimal cost. As mentioned in the “Cost and Budget” section it is important to maintain your investment into the system proportional to the benefit. Outsourcing to freelancers can be a convenient way to start recollecting data with minimal investment.

Freelancers also have a place as support to an in-house data science team, in a team augmentation model. Your in-house team may find bottlenecks at critical points in their process, a freelance hire to take care of repetitive tasks during these times may help alleviate the pressure and keep the in-house team on track.

When building an in-house data science system, freelance work also allows you to assess candidates before hiring them full-time. A sustainable growth model for your company could be to start covering new functions with freelancers in order to integrate them full time with minimal onboarding once these functions begin to have an impact and grow more complex.

Pros

• A quick and agile hiring process
• More affordable starting prices
• Reducing burden from your in-house staff
• Assessment of prospective new hires

Cons

• Risk of having the project change hands especially for maintenance, resulting in unreliable results or production bottlenecks while the new engineers decipher the code of the previous freelancer
• Less specialized skillset
• Communication can be hampered by issues in availability and working patterns
• Quality isn’t guaranteed when hiring a freelancer for the first time
• Less dependable security

Hiring a Remote Data Science Team

If hiring a freelancer is like onboarding a new employee, then hiring a remote team is like absorbing a new department. This can sound daunting, but if you need help selecting the right company, tell us what you need. We can do the work for you and connect you with up to 5 companies within 72h that match your need- all for free.

A specialized data science development company can offer a process that covers the complete lifecycle of your application from initial data mining to deployment and support, the required specialization and previous experience will depend on the scale and variety of data they are collecting and your objectives — if you are building autonomous vehicle systems or using medical imaging to diagnose cancer, you may have more challenging asks from their data scientists than if you are an e-commerce company trying to figure out why customers are abandoning their baskets.

Scalable engineering made easy to support, upgrade, and extend will pay for itself in a short time frame. With a full-scale development team, that expertise and specialised skill set is available in full. Built-in is a commitment to quality control and future support the scalability of your ML model.

The specialisation and commitment of a larger remote team means that each area of the application benefits from dedicated professionals with a rich experience to draw on. Solid understanding of ETL/DWH processes and tools, SciPy ecosystem for data transformation, and robust programming skills - areas that face notoriously difficult issues - benefit from expertise and knowledge, building a dependable algorithm-based system.

Pros

• Well suited for enterprise or long-term projects -- most of the time clients ask you to be on-site as you will work closely with their teams and their secured data
• Business intelligence, Big Data Analytics, Artificial Intelligence (AI) / Machine Learning (ML), Database Admin/Architect, and Data Scientist all in one package.
• Security in place to facilitate Non-Disclosure Agreements (NDA) and Non-Compete Agreements (NCA) to protect intellectual property
• Teams tend to stay more up-to-date with the latest tools, technologies, and thinking to build scalable applications
• Professional experience creating a model validation and interpretation throughout many industries
• Dependable support & maintenance for the model framework

Cons

• More expensive option in the short term
• A more formalised process that generates paperwork and documentation— not suited for zero to one product companies and short-term exploratory work

Conclusion

For any company that wishes to enhance its business by being more data-driven, data science is the secret sauce. Data science projects can have exponential returns on investment, both from guidance through data insight, and the development of data products.

While freelance hires may be a smart and cost-friendly idea to get off the ground setting up a data recollection system at the beginning, it will be important to scale the operation into a full-service team or build an in-house team to develop data products and provide maintenance to assure accurate results.

Data is the lifeblood of many businesses, and it is important to assure it stays in and is handled by safe hands.