What qualities are required of a great data scientist?
Beyond technical ability, one needs to have mastery in some important non-technical skills. A thoroughbred data scientist must present him/herself as someone versed in areas like communication and presentation. The rise of big data and the proliferation of tools for manipulating data have presented a new challenge that leads to ineffective data communication. My list was created to assist data science enthusiasts in making excellent hires, and I will be touching on both technical and non-technical skills.
1. Good background in Statistics
A good data scientist should be able to model any dataset and use a collection of algorithms to make statistical predictions and recommendations. A great data scientist, however, can detect something ‘fishy’ in the results they receive, recognize the need to ask the client or stakeholder a few more questions, and distinguish between a game-changing insight and an expensive blind hunch.
Since the basic task of a data scientist is manipulating data, statistical knowledge is at the top of our list. Knowing your algorithms and when to use them is arguably the most important aspect of a data scientist’s job. However, doing so well can be both an art and a science.
2. Technical knowledge
In order to create tools, pipelines, packages, modules, features, dashboards, websites, and other things, data scientists collaborate with diverse teams. The front and back end hooks have their own peculiarities. This means that data scientists carry out structured and unstructured tasks; when they can’t find an answer, they sift through obscure formats and antiquated code.
A great data scientist has the spirit of an ethical hacker. The industry’s gold standards are changing at an alarming rate, and technical flexibility is just as crucial as experience. Data scientists collaborate, support open source, and share knowledge and expertise to ensure that they can respond quickly to market demands.
3. Be Creative
Creativity has applications far beyond the obvious in a systems development cycle. A data scientist who can produce an appealing and easy-to-understand report or visuals that would require a master’s degree to fully comprehend is a skill with enormous payoff. Creativity provides the fuel for effective communication, which is not a difficult sell.
The best data scientists are creative problem solvers with an unorthodox relationship with the word “no.” Great data scientists are bothered by “no,” so much so that they find a way around it, over it, or through it, or they back up and take a different path entirely. Design constraints are both vexing and enticing. A data scientist who says “no” quickly followed by “wait, hold on… let me think” may be a great creative thinker.
Some processes require attention; messy data that doesn’t quite fit into a model the client had in mind needs to be structured and missing data address; dead ends, wrong turns, roadblocks, and red tape; teams full of mixed agendas and personalities; budgets, deadlines, clients, teams, and contractors; and the unicorn-magician-programmer-statistician, etc. require a strong personality to handle. Those who make it through have a healthy reserve of grit.
All of these obstacles and challenges could force a reasonable person to take an unplanned leave of absence. Grit is the inner drive that propels us over obstacles, recasts setbacks as design constraints, propels us through fear of failure, keeps us walking through actual failure, assists us in resisting the urge to take things personally, and brushes dirt off our shoulders. When grit is working, we are less competitive, which allows us to encourage and learn from one another. We develop a desire to explore the new and unknown.
5. Communication/Presentation skills
When the analysis is finished, the results are usually not pretty. That’s not to say they’re useless, but they’re frequently trapped in opaque readouts or plots that make sense to the expert but look like hieroglyphics to the rest of the team and stakeholders. Algorithmic output must be interpreted and communicated before it can leave the data science team and enter the hands of the rest of the company to be put to use in accordance with its usefulness.
A great data scientist can contextualize and translate a problem and its solution to people from all walks of life by using common ground, metaphor, astute listening, and storytelling. Written communication for a statement of work or a report, visual communication for clear and intuitive plots and visualization, and spoken communication for presentations, project specifications, check-in meetings, and iterative design are all examples of this. If your data scientist can call a meeting to a halt when it is clear that not everyone is on the same page, draw a diagram on the whiteboard, and elicit consensus from a diverse team, you have a highly valuable member on your team.
Because collaborations with various stakeholders such as data engineers and business executives are common, being open-minded allows a data scientist to work productively with others. The state of open-mindedness allows a data scientist to suspend judgment and continue to explore the best possible solution. Even though we are working with a hypothesis, there are many other hypotheses that could lead to more accurate results. As a result, a great data scientist is an open-minded data scientist who notices new emerging patterns even if they differ from initial predictions.
Because programming is a necessary skill in data science, debugging is an unavoidable step in designing a data science solution, from data processing to performance evaluation. The combination of programming and data science technical breadth, on the other hand, introduces significant complexities in coding a data science pipeline, necessitating data scientists to pay attention to the smallest details. It is not uncommon for a minor coding error to escalate into a critical problem that results in unexpected analysis results. A detail-oriented data scientist often spends a significant amount of time examining the quality of data prior to feeding the data into machine learning algorithms, in addition to diligent debugging in programming. As a result, being detail-oriented aids data scientists in producing high-quality work.
Due to the rapid growth and constantly changing characteristics of the data science field, data scientists must have an unquenchable enthusiasm for learning. The desire to learn and understand new data science techniques is an important factor that constantly aids data scientists in improving their analytical capabilities. The accumulation of collective knowledge allows us to recognize the logical connections between various bodies of knowledge. Furthermore, inquisitiveness is the expression of the desire to inquire and investigate, which aids data scientists in avoiding cognitive biases when solving problems. When we see a correlation between two variables, we have a tendency to conclude that causation exists.
Inquisitiveness is one of the invaluable soft skills for success. Many people who are drawn to data science find it most appealing to work on a constant stream of new and challenging puzzles. They are people who have been asking “why” and “how” since they could open their mouths. They take on requests, implement it, and confidently deliver the prediction or analysis. A great data scientist will ask for more data, to interview users, or to try something new in the next iteration because something he did piqued his curiosity. Curious data scientists may dislike machine learning competitions because they do not have access to all of the levers and decision points to ask questions and dig deeper. Curiosity masters are quick to question their own assumptions.
To be a great data scientist, you must go above and beyond what you have been doing in order to achieve previously unattainable levels of improvement.