Who is a Great Data Scientist?
You have invested time building up your technical knowledge and expertise in the data landscape, Congratulations:
How do you keep excelling at it and prove your value to produce meaningful business impact?
I will be sharing general summary of the qualities/ traits/ mindsets that I have observed /picked from some great data scientists in their work and also from reading the (The Data Science Handbook: Advice and insights from 25 Amazing Data Scientists by Carl Shan, William Chen, Henry Wang, and Max Song), that makes them stand out and differentiates them from good data scientists and how these traits can be learnt/acquired.
- They have intrinsic drive to deliver value to the business and think outside the box to make sure that the whole team has the highest impact.
- They ask their managers on project requirements, do their research and deliver a clean and thorough analysis, communicate their most important findings clearly and then ask what they should work on next.
- They think about their work within a deeper context (the wider data ecosystem). They ask and seek to understand the larger goal of the project, challenges and core assumptions.
- They are strong critical thinkers with a knack for active listening that helps them understand various business pain points.
- They have a deep rooted paranoia about data quality. They take time to understand the data and its generating process.
- They have ideas for how a project could also, benefit other parts of the economy or raise awareness for potential conflicts that could occur down the line.
- They are informed about new frameworks/ techniques that come out and suggest innovative ways on how they could be used to improve the process.
- · They are genuinely interested in how the business as a whole work and actively build working relationships, talking with people to understand their perspectives.
- · They are full of ideas of what they should work on next and pride themselves on how many people they have helped and not how complicated the techniques they used were.
They do all these because they care about the impact of their work and not because they want the attention or boost their career. They don’t start a discussion just for the sake of having a discussion and they are able to step back when their opinions don’t resonate with others.
How do you acquire these skills to become a Great Data Scientist?
- If you don’t have a data science job yet, it’s hard to prepare for this. But you can be reading blog posts in which companies from different industries share how they get value out of their data (plenty of those on Medium).
- Through mentorship with experienced data professionals in the industry.
- Once you have a job, ask as many questions as possible without being too annoying, and talk to everyone in the company. Don’t be shy and just sit in front of your data. Constantly ask yourself, why you are doing what you are doing, if it still makes sense or if there is a better way.
- Get involved, be curious and let others know about your thoughts. The more feedback you get on your thoughts, the better you can fine-tune your work on the business context.
Featured Image: Pixabay
Kennedy Wangari is a highly talented and dedicated Natural Language Processing Engineer, an AI Community Leader, and Innovator.
Check out what books helped 20+ successful data scientists grow in their career.
His research work focuses on tackling problems in Natural Language Understanding majoring in Automatic Text Summarization, Question Answering, and Information Extraction areas.
He takes leads in championing Artificial Intelligence for Social Good, tackling and solving complex business and societal problems/challenges through the lens of the UN SDGs. His work is geared towards making real-world social, health, wellbeing, and financial impact for companies and people’s lives, and bringing the world into a more equitable, prosperous, and sustainable path.
As an AI Community Leader and Innovator, he is passionate about tech communities, innovations, and harnessing the power of data and AI technology to make a better and easier tomorrow.
He is actively involved with various AI Hubs and data-centric communities (AWS Educate Cloud Ambassador Program, deeplearning.ai Ambassador, and the Google Developer Student Clubs), empowering, supporting, mentoring, and strengthening them, to build, innovate, shape, transform and ride with the wave of Artificial Intelligence.