11.07.2022 Executive Data Bytes – Hiring an in-house data scientist - Is it the road to perdition?

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we are asking the question: “Should I have a data scientist on the payroll, or is it better to outsource?”. 

Focus piece: “Building your data science team: in-house or outsourced?”

Executive Summary

Building a data science team is one of the most essential elements of developing your AI tool. The question is, do you start recruiting or outsource? This Neoteric article lays out the pros and cons of each option, to make your decision a little easier.

Key Takeaways

• For many companies, building an in-house data science team seems like the only option out there. However, for small and medium companies, this is often not an available solution.
• Hiring a data scientist can take over 20 months and cost 15 thousand dollars. And hiring one person doesn't solve the problem - when that person quits, you're left with nothing.
• Outsourcing data science helps you start your AI project faster and save money. You don't need to spend months on recruitment and you can stay ahead of your competition.
• Since you don't wait long to hire a team, you reach the goal faster, and you can change the size of your team depending on your needs, saving you money.
• When looking for an outsourcing company, you need to make sure that they have the domain expertise you need. This is done by communicating with them, asking and answering questions, mapping processes, and writing things down.

Focus piece: “Why you probably don’t need to hire a Data Scientist (yet)”

Executive Summary

The key issue with hiring data scientists to solve all of your data problems is that they may not have all of the skills you need. This article published in Towards Data Science explains why you should focus on data infrastructure and decision-making before you start interviewing.

Key Takeaways

• The exact responsibilities across data titles have been constantly changing over the last few years. This has led to companies not being sure about the difference between all the data roles, particularly at startups or early-stage companies.
• Unless you have an AI-centric product, you need to be able to drive business decisions at a fast pace by pulling numbers and answering cause-and-effect questions. 
• A typical high-level data-driven roadmap can be broken down into 3 main stages: dashboarding, strategy recommendations, and machine learning and automation. Many data scientists have the ability to cover multiple roles in their toolbox but may not have as much experience orchestrating data pipelines as a data engineer.
• The rise of data science has made companies keen to hire data scientists to cover all of their data needs. Companies should define what stage of the data-driven roadmap they currently sit at.

Focus piece: “A Complete Guide to Data Science Outsourcing 2022” 

Executive Summary

Outsourcing data science can be a great solution for companies that want to gain access to data scientist skills without hiring a full-time data scientist or training someone in-house. Before you outsource your data science work, read this article from Inapps, and make sure you understand what data science is and what you need.

Key Takeaways

• Outsourcing data science can be more cost-effective than hiring in-house staff, can improve efficiency, can give access to specialist knowledge, can help businesses scale up or down their operations as needed, and can help businesses access new markets, customers, and strategic business opportunities.
• Working with experienced data science outsourcing companies can help you avoid potential pitfalls of potential miscommunication, wasted time, and money.
• When businesses outsource data science, they lose some control over the work being done, may have cultural differences, may have data security concerns, may not have complete control over the resources being used, and may have difficulties integrating the work into their own data storage systems.
• Before you sign on with any company for outsourcing data science, define key performance indicators (KPIs) to track progress.

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