Many technology startups find it difficult to provide successful and well-organised data science programs. Felix Hoddinott, chief analytics officer at Quantexa, gives his view on how to create a good team for your business.
In order to achieve an effective data science capability, startups need to recruit a mix of experienced staff as well as junior hires, concentrate on fostering a positive and nurturing environment in which your team can develop, and ensure your company utilises the right skills, data, technology and direction to be a success.
Upskill your workforce
Ensure you continue to give your staff opportunities to gain new skills. This could involve encouraging them to attend new training courses, as upskilling your workers will help your business stay ahead of the curve. For specialised skill development, however, you should implement your own training courses. It’s vital to bring all your staff together to help design and deliver the courses so that all new areas are covered.
Mentoring is also important in order to support workers with their development and help your staff achieve their goals, ensuring they stay inspired and focused on their next steps. A good way to develop a person’s skillset is to ensure that more junior staff collaborate with senior staff on as many projects as possible so that they can learn and grow with support and guidance. This helps ensure that as a company, you are fostering a space where talent is nurtured and where team-work, support and transparency is valued, so that staff feel supported.
Hire staff with a range of skills
This week’s technology headlines
Creating a data science team that delivers is difficult, and many startups fall down at the first hurdle: employing the right people. Keep in mind that a workforce, like any team, needs a range of skills sets within it to function effectively. For a data science team, these should include:
- Business analysts – The best business analysts come from a technical background and are vital for knowing how to put in place data science methods. Good communication is also needed in this role so that your end user comprehends the solution properly.
- Data architects – These team members are needed to unite the operations systems and the security around the project, and will need to be able to make important and relevant decisions based around data.
- Data scientists – Useful data scientists need to demonstrate expert understanding of data analytics and machine learning, as well as possessing strong communications skills.
- Data engineers – These staff members are crucial for implementing the solution. They need strong development skills combined with the ability to understand and analyse data so they can scale complex algorithms.
Hire from a range of specialities
As well as a solid combination of skills, your team should also display a balance of experience. When hiring junior staff, don’t simply choose based on technical flair, but consider candidates who have the ability to think through a problem and show potential. Technical skills can be taught to the right staff in weeks, whereas it’s much more difficult to teach logic and abstract thinking.
Make sure you’re looking further afield than just data science degree graduates – they may know about the technology you use but that doesn’t mean they understand how to apply it. Some of your best candidates may come from a background in subjects like Astrophysics, which requires the skill to look at a problem as a whole and describe it in a series of logical statements – proficiencies that are essential to becoming a data scientist.
ClearCourse acquires digital fundraising company instaGiv
Build trust with clients
Effective data science involves accessing high volumes of real data, but clients sometimes request anonymous data in order to increase security which can impact the quality of the data solution. It is important to be as transparent as you can with your clients about the impact of anonymising data on the quality of the solutions and the growing cost of development. This is where building a sense of safety comes in, so that clients and staff trust each other to work together.
Ultimately, an effective data science team is generated through identifying suitable staff, supporting them with the right opportunities for growth and upskilling, and giving them the chance to develop and push themselves for the benefit of the company. The right hires and smart management of your team, as well as good technology, puts a startup on the right path for the future.