Manners (and having the chops) maketh the manager

Your next manager may already be working in the business. Who should you look out for?
9 November 2018

Amazon CEO, Jeff Bezos. Source: AFP

A recent blog post at Domino by Ricky Chachra, a research science manager at ride-hailing company Lyft, advises companies looking to grow their promising individual contributors into effective managers. While the post is specifically concerned with data scientists – a rapidly expanding position in many tech companies – some of the lessons he expounds will be useful to any organization looking to promote its technical staff into managerial roles.

Chachra’s own journey started as an individual contributor, and through the company’s strict appraisal and mentoring system, he gained his present position after no small personal sacrifice and hard work.

The four mainstays of data science managers’ abilities that Chachra details are below, but for this article, at Tech HQ we’ve expanded them out, so they are relevant to a broader church than data science – companies looking for internal candidates in IT management may wish to read on…

1. Mentoring skills

To be a good mentor, technical staff, such as devops, cybersecurity personnel, systems & networks administrators, compliance specialists and database managers need to have what Chachra restrainedly calls “impactful […] skills at a level well above foundational.”

In short, this means that to succeed in managerial strata running any technical department or function, managers need to have ‘made their bones’ in the particular discipline, or a closely-related field.

The ability to grasp the concepts that pose problems and present opportunities for staff in his or her charge is of paramount importance. And the manager or supervisor needs to be able to advise and guide staff, often through highly complex technical challenges. Expecting to parachute in a technical novice is usually destined for failure, except in the most exceptional of circumstances.

2. Prowess in managing interpersonal relationships

This ability comes primarily from within with the prospective manager’s emotional intelligence, which can only be nurtured, not created, by the organization.

The ability to be dependable to other managers and higher-ups needs to go hand-in-hand with a collaborative skill to empower inter-departmental relations.

Additionally, managing teams depends on being able to empathize with the human, everyday situations that staff members find themselves in. Those without the necessary life experience here will falter.

3. Communication with technical and non-technical audiences

While the knowledge required to communicate with technical audiences should be already taken as read (see point 1. above), the skill of communication at all levels is critical.

In short, knowledge is one thing, the ability to impart that knowledge clearly and concisely is entirely different. And in the case of non-technical staff, those communication skills need to trad the fine line between revealing the necessary level of complexity without neither patronizing nor losing an audience.

4. Seeing the business picture

Drawing out the required technical excellence from a team or entire department is a fantastic skill, but it needs to be coupled with an appreciation of the context in which a specialized function operates, in business terms.

The business needs have to dictate the overall direction taken, and the economic consequences of any decision need to be considered.

While striving for perfection and ascetic beauty in complex code, for example, may be a thing of wonder to dev ops staff, perfection has a price tag in terms of resources – especially time.

The business imperative should be apparent, therefore, to the manager, and through him or her, clear to the team being led.

What are your thoughts about what makes a good data scientist-come manager? Share them below.