Data Scientist 2.0: The Evolution of the Role and the Skills Needed to Succeed

Data science has rapidly evolved over the past decade, with the demand for data scientists skyrocketing and the job market growing at an astonishing rate.?

In today's data-driven world, companies across all industries are looking for skilled professionals who can harness the power of data to drive business decisions and growth. But as the data field evolved, the responsibilities and skills required from a data scientist have also transformed. This article will explore how the role has changed and what it takes to be a Data Scientist 2.0.

1. Specialize in one area

It's important to specialize in a specific area rather than trying to be a jack of all trades. Data science can be compared with life sciences or medicine in that they consist of a wide range of practices. It's important to master the basics first and then focus on specific platforms, tools, and areas of expertise to become a specialist in your field. This will allow you to excel in your chosen area and make a more significant impact in your organization.

Consider also the fact that companies hire data specialists to solve particular, sometimes even niche, tasks. This especially can be applied to Data Scientists aiming to become Senior level with orientation for product companies rather than services.

2. Understand business goals

As a data scientist, it's important to understand the business reasons behind the choices you make. This is one of the reasons why Business Scientist is one of the expected professions to appear (https://www.dhirubhai.net/pulse/misuse-terminology-data-field-job-descriptions-ivan-reznikov/). Having a clear understanding of the objective before analyzing data is incredibly valuable. Instead of aimlessly searching for patterns, using a scientific approach is more effective by formulating hypotheses based on formal models of human behavior, economics, systems, etc., and testing those hypotheses. This will lead to more successful data science applications.

Try implementing machine learning methods with clear objectives. Start by considering how the customer experience will be improved at a high level and obtain a straightforward narrative linking the business problem to your choice of algorithms.

3. Invest in cross-departmental expertise and boost soft skills

Having cross-departmental expertise is very beneficial for data scientists.?Best data scientists act as the bridge between technical and non-technical teams. Therefore, in addition to having a solid technical background, data scientists should also have domain expertise in the department or area they are focused on, such as product, marketing, sales, or finance.?

I've seen and mentored specialists that made domain knowledge their primary weapon, and it successfully worked for most of them. Your unique background and a blend of skills can be a strength in data science.

Cross-departmental expertise

Besides domain knowledge, data scientists should have a basic understanding of mobile, backend, frontend, and ops development. It allows them to understand better and communicate with the engineering teams they work with. Knowing how to code and design interfaces can also enable data scientists to build their own tools and prototypes, which can be used to test and validate their models and ideas. Besides, it allows us to understand and anticipate different data collection methods' technical limitations and opportunities and make more informed decisions about which tools and technologies to use in their projects.

Collaboration and soft skills

Collaboration is essential to data science, as multiple departments work together on IT projects. Collaborating, compromising, and setting clear boundaries and expectations are necessary. A common challenge I've previously seen data scientists face is facilitating cooperation between departments in collecting and interpreting data. Predictive models and historical analyses are only as powerful as a team's agreement on the validity of the source data. Misalignments often occur between the engineering and data teams, so there needs to be harmony between the two to allow the data science team to collect and analyze quality data accurately.

The same goes for the data science outputs. Consider upfront how often your model will be used and the expected result. For example, when designing a model that was planned to work in real-time, I went with a more lightweight method, as this was the proper tradeoff we agreed on with multiple teams.

4. Communicate effectively with non-tech/ds colleagues

Explaining technical concepts to non-technical audiences is crucial for data scientists. However, it may be frustrating for data scientists who are accustomed to using technical terminology it needs to be more. The data team needs to communicate effectively with audiences from other departments, as well as executives and stakeholders, who may not have a strong understanding of the intricacies of the job. Keep in mind that stakeholders are usually less interested in tech and instead look for key findings and action items.

A modern data scientist who cannot clearly explain their model and its value to business stakeholders will likely face challenges in achieving success. To improve this area, it can be helpful to practice explaining data problems in a clear and easy-to-understand manner, similar to how you would explain it to a family member at a holiday dinner or if they were five.

5. Dedicate time to data handling

A significant part of a data scientist's job is working with raw data. One of the primary challenges is figuring out how to use data, sometimes including extracting, cleaning, analyzing, and getting insights or building models from data. Working with raw data is often the unspoken bulk of a data science job. A data scientist's effort is creating a clean data set with useful information before any machine learning or statistical models can be applied, which is often considered an art or a craft. Like any artist or craftsperson, there is much unseen effort into the final product. Data can be incomplete, inconsistent, or biased, making it difficult to draw accurate conclusions even during the discovery phases.

With the increasing volume of data generated, data scientists are often faced with the challenge of handling big data. This requires specialized tools and techniques to process and analyze the data promptly and efficiently.

6. Be adaptable and aware of context

Being rigid in your approach to problem-solving can limit your potential for success. Keeping open-minded to different methods and adjusting your approach based on unique circumstances may lead to the best outcomes. Effective data scientists are aware of multiple ways to approach a task, each with its advantages and drawbacks, so that they can choose the most appropriate method for the specific context. This is one reason why you need to pay attention to different approaches while studying or building your career in data science. You might not need some of them for a long time, but this helps you keep the options open.?

Keep up with new technology. The field of data science is constantly evolving, with new technologies and techniques always emerging. Data scientists need to stay up-to-date with the latest developments to remain competitive.

7. Regularly maintain and document work

Regularly maintain your work, as small mistakes can be costly. Catching them early is crucial. Invest the time in refactoring your code, validating data sources, and documenting changes made. Always keep track of the updates made, as production and stage are different, close to how practice and theory are. Hidden data dependencies, unstable data sources, and undocumented assumptions can lead to unexpected changes in your results when retraining models.

This is especially important for data scientists that are part of larger technical teams. In these cases, you might be the only person doing data science as part of the solution.

8. Consider Ethics

Making sure that data is used in a way that respects people's privacy is one of data scientists' most important ethical considerations. This entails ensuring that private information is shielded from unauthorized access and that users are informed of how their information is being used. Data scientists must also watch out that their work does not reinforce power disparities or favor particular groups of individuals. Think about any possible unexpected repercussions of your work.

The algorithms and models data scientists develop must be fair and unbiased to avoid, for instance, sustaining preexisting biases or discrimination. It is crucial to regularly analyze the model performance and make adjustments as needed to prevent unintended outcomes.

9. Do Data Science

I've seen that sometimes one aspect gets overlooked after reading articles similar to the one you're reading now. You might start digging deeper in one direction, understanding business needs, and working on communication and soft skills.?

But remember that a data scientist's primary responsibility is to do data science. It involves statistical, machine learning, and data analysis techniques to extract insights, knowledge, patterns, and trends from data: result orientation is a must.

Another pure data scientist's role is to develop and apply ML models to make predictions from data. This includes selecting appropriate ML algorithms, training and tuning models, and evaluating their performance. You might also be the person to go for designing and implementing experiments to test hypotheses.

For many companies, data science is still unknown and in the experimental phase - "shall we apply?" and "should we hire?". They will require a data scientist to show the field's potential for their particular case - so mastering pure data science is essential.

Results

Let's create a table mapping the results of Data Scientist 2.0 profession.

Today data scientists are judged not only by their code or statistical and ML background. The reality is that data scientists spend a lot of their time handling large and complex datasets, sometimes cleaning them more than doing pure ML. But don't underestimate the role of soft skills.?

Data scientists are expected to have a strong understanding of business goals and problem context. They are required to communicate effectively with other tech and non-technical colleagues and be responsible for the results they provide working as part of the team.

Back to the origins

The role of a data scientist has changed remarkably in the past decade. In the early 2010s, data science was still a relatively new field focusing more on tech aspects: code, statistical, and ml techniques to analyze data. Research and experimentation were given their first green lights, as one of the primary goals for data scientists was to uncover insights, trends, and patterns.

As data science matured and became more widely adopted, the field's focus shifted mostly toward business applications. Companies began understanding the value of data science in driving business decisions and began to invest more in data science teams. As a result, data scientists are expected to have a strong understanding of business goals and objectives. Effective communication with non-technical colleagues and involvement in decision-making started to become regular skills.

The increased focus on business led to a greater emphasis on collaboration and soft skills. Data scientists now work closely with other teams, such as product managers and marketing teams, to understand their needs and develop data-driven solutions.

Conclusions

Overall, the role of a data scientist has evolved significantly over the past decade, moving from technical becoming more business-oriented with a broader set of skills.?

Are there other skills you believe are required from a modern data scientist? Leave a comment below!

Reference

https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

https://venturebeat.com/social/these-are-the-skills-you-need-to-be-a-data-scientist-at-facebook/