by: Zank, CEO of Bennett Data Science
Let’s say you want to start small, from one to four people, as you build out a data science team. This week, I’ll talk about how I’ve done just that, many times over, with great success for big and small companies. The key is to start small, hire strategically, and grow around a strong initial data scientist.
For your first hire, especially if this person will be the sole source of your analytics efforts for an extended period, find an experienced (senior) generalist with relevant experience; someone who:
- Can explain data science benefits and complexity to non-technical stakeholders
- Has worked in your industry, or related
- Has worked with IT to get and clean data
- Has experience building/deploying simple machine learning models
I can’t emphasize enough how important it is to find a good communicator for your first hire. During the interview process, ask the candidate to explain her work. Do you “get” it? You should, and nearly immediately. If the candidate is having trouble explaining something on her resume, imagine how that will play out when she’s in a time crunch, trying to explain a last-minute model change to the dev team or revenue loss to the CEO. There are lots of guides to effective hiring, so I’ll leave it there.
For the next hire, I recommend bringing on two roles, to form what I call The Magic Three. From a high-level, here are the roles, starting from the first hire (Sr. data scientist):
- Senior Data Scientist – manages and coordinates the efforts of the team, including translating business needs into AI objectives, while handling all the ML modeling
- Data Engineer – in charge of making data available, working with the senior data scientist to build and administer ETLs for entire team
- Data Analyst / Jr. Data Scientist – responsible for informing best methods for ETL, providing insights to the company and supporting the senior data scientist day to day
I’ve found data engineers to be invaluable in this scenario, and often times, the next hire or two beyond these three is another data engineer. This is a nod to the complexity data scientists face when cleaning and handling large amounts of data. A good data engineer working closely with a data scientist can alleviate a lot of that complexity. The analyst position is largely a support role and is very valuable as a source of help to the other team members and to provide data driven insights throughout the company.
There are many other titles out there and some may be useful to your company, depending on your situation. Far from exhaustive, here are some other types of analytics professionals with general descriptions:
- Data Scientist(generalist) – can handle all the tasks done by the specialists below, but generally prefer specializing in one area
- Machine Learning Engineer- works with models, objectives and metrics
- Data Engineer- works with data and pipelines
- Statistician- understands relationships between data and business logicand how this informsbettermodels
- Data Analyst- adept at understanding and presenting trends in data
To recap, start with a strong data scientist in a central role and when you’re ready, go for The Magic Three. Build out carefully around this group, emphasizing the needs of your particular organization.