Six must-have soft skills for data scientists
Anaconda report on the state of data science 2020 reveals these 6 soft skills that data scientists must have to create an impact within the organization.
According to the State of Data Science 2020 report conducted by Anaconda among 2,300 working data scientists, students, and academics in 100+ countries, there were 6 soft skills that data scientists or whoever working in data must have in order to achieve success within the organization.
In this short blog post, I attempt to tell their findings again with visuals and some of the resources that I came across on the Internet that might help data scientists beef up the communication skills. The quotes are from their report or their blog post.
One concerning finding was that 40% of respondents said they “almost never” or “only sometimes” can effectively demonstrate the business impact of data science within their organization.
This hurdle often has less to do with technical skills and more to do with soft skills like communication and relationship building.
1. Prioritize the right projects
To be able to show business value, you must first identify projects that ladder up to broader business priorities. While a project may be interesting or valuable in another way, if it doesn’t help achieve identified business goals, it is going to be an uphill battle securing stakeholder buy-in and showing meaningful results.
Like anything in life, we need to learn how to prioritize because everything is limited e.g. time and money. Everyone might have known about the pebble jar analogy to the prioritization.
If you want to fit the most stuff in the jar, you put the rocks in first, then the pebbles, then the sand. If you put the sand in first, somehow there’s not enough room for the rocks.
2. Bring stakeholders in early
Folks from IT and product are obvious stakeholders, but data scientists should go a step further by identifying executives with an interest in the project’s success and securing their sponsorship. Executives can help identify and engage other stakeholders, ensure a project is aligned with larger business goals, and remove obstacles.
3. Set clear KPIs and provide regular updates
However, it is also critical to tie your project to KPIs that reflect business goals and metrics. This may require translating data science KPIs into language that internal, non-scientific audiences will better understand like cost, revenue, reduced churn, or time saved.
I recommend you check out the resources from WhatMatters.com website, subscribe for their newsletter and their companion book “Measure what matters” on the OKR methodology for setting goals and providing updates.
I was assigned to working on this bank for the past 2 days only but every day around 6:00 pm my team has to jump on a call to update the head of data science on what we have done in the day as a way to inform him on small wins we achieved, hurdles we encountered and hopefully they can provide some feedback.
4. Achieve and communicate quick wins
As a project gets up and running, getting a quick win can be a crucial way to maintain momentum. A quick win means a goal that connects to business priorities that you can define, reach, and achieve within 6-12 weeks. Quick wins keep stakeholders invested and show your organization that you are delivering immediate value.
This is what I edited on the photo that I captured the moment of my colleague was documenting what he could have done in a Jupyter notebook. This will inform the users from the client able to know what to follow and replicate.
5. Know your audience
In our survey, nearly a quarter of respondents said that the data science/machine learning area of their organization lacked communication skills. Data science teams can often be isolated from the rest of the business and most comfortable using technical language that others may not understand.
You should definitely check out the course Communication Foundations and Effective Listening from LinkedIn Learning.
Those two short courses will ground you with the fundamentals of how to communicate effectively in different context like office, in the lift or social context, sending the email. I have to admit that I completed those courses but I feel that one must keep honing this important skill every day in order to master it.
6. Visualize it
Similarly with communication skills, 24% of survey respondents said that their data science team lacked data visualization skills. Moreover, only 49% of the students who took the survey said that they were being taught data visualization in school.
I recommend you check out the blogs, resources and books from Duarte consulting firm on telling stories and visualization.
Comments are welcome if I was wrong or missed out something. If you come across any useful content tied back to this post’s topic, please do comment as well. Thank you for your time reading this blog post.