Leadership
leader /'līdәr/ n: Anyone who holds herself or himself accountable for finding potential in people. - Brenè Brown
Leading Data Scientists
For me, it is constant learning how to lead a team of data scientists and how to be the kind of leader that I want to be. Because I have always worked with data, I don't want to step on other people's toes. Being too involved, micromanaging, often stifles creativity and unique solutions. Instead, as a leader I am accountable for finding and nurturing potential in people.
Finding Potential
It is important to give people the opportunity to excel and to demonstrate skills. Recently, I gave a data scientist an opportunity to mentor a new employee (a new data scientist to our team). He rose to the occasion and many on our team, including my VP, was surprised. Why? Mentoring was a skill he was never given the opportunity to demonstrate.
He continues to demonstrate skills he was never given the opportunity to demonstrate.
Another data scientist lead the evaluation of the new tax reform legislation. This required him to meet and work with tax attorneys. I could have coordinated this effort, but allowing him to do it gave him the opportunity to learn and demonstrate soft skills.
He continues to demonstrate skills he was never given the opportunity to demonstrate.
Another data scientist lead the evaluation of the new tax reform legislation. This required him to meet and work with tax attorneys. I could have coordinated this effort, but allowing him to do it gave him the opportunity to learn and demonstrate soft skills.
Formulating The Problem
One of the most important things to do as a leader of data scientists is to help formulate the problem. Tom Kelly, author of "The Art of Innovation" writes that too often people come with the solution and not the problem. This is especially true with data science. Many times we are forced to take a step back and ask, "What are we trying to figure out?" or "What is the problem we are trying to solve?"
Encouraging Failure
Most of what we do in data science ends up as a dead end. Failure, finding these dead ends, is a great way to learn. Without failure, experimentation, it is often hard to learn.
Making Hero's
I often say, "It is amazing what can be accomplished if no one cares who gets credit." That being said, it is important to let your data scientists be the heroes. They are the ones that need to be presenting findings to clients and to executive management.
Create a Learning and Sharing Environment
As a leader of data scientist's I try to foster an environment where there is constant learning. The tools and techniques are ever changing and evolving. I try to organize the workload, so my team is afforded time to experiment, discover and share new insights.
A Leaders Role
As a leader of data scientists, I need to step away from the code and the work. I do poke around in the data because it is something I love to do. But as a leader my attention and focus needs to shift from the work to the development of individuals.
Besides developing people and finding potential, I spend a good deal of time thinking forward, managing the pipeline of work and selling my teams capabilities. If I don't think forward and manage future expectations, my team will be run over when the future arrives.
Besides developing people and finding potential, I spend a good deal of time thinking forward, managing the pipeline of work and selling my teams capabilities. If I don't think forward and manage future expectations, my team will be run over when the future arrives.