Honest Review of Working as a Data Scientist

My takes after working professionally for more than half a decade

Data science has become an innovative field in the business where strategic decision-making relies upon. By combining statistical analysis, machine learning, data visualization, critical problem solving, and business knowledge, data scientists could provide value to the company. This is why data science has become one of the most sought-after professions in the century.

However, what is it like to work as a data scientist? I have been working in data science for quite some time, and I have some insight you might want.

Here is my honest review of working as a data scientist.

Way to Become a Data Scientist

If you have known me for some time, I don’t have an education in data, statistics, computer science, or any IT-related major. Instead, I have a biology education and a master's in evolutionary biology.

Some usually ask, how can I make it into the data science field from the biology major? It’s not a question I could answer directly because many layers are involved. But, I can summarize them as a combination of consistent self-learning, great networking, and confidence to keep moving forward.

However, I want to point out that everyone could try to become a data scientist. It doesn’t matter what your educational background is; you can always strive to become a data scientist. To become one, though, might require motivation, consistency, and some luck.

Some skills I am trying to learn to become a data scientist, including:

1. Programming Languages (Python, R, SQL)

2. Machine Learning and Advanced Analytics

3. Data Wrangling and Visualization

4. Big Data Technologies

5. Problem-Solving

6. Critical Thinking

7. Domain Knowledge

I tried to focus on it initially, but it evolved when I got into the data scientist job. If you need some direction, you can see my free Data Science beginner data science skills learning path.

That’s some story, but let’s return to my review of working as a data scientist.

Data Scientist Activities

I have experience working as a data scientist and could call myself a professional. I still have so much to learn as I know that the data science field is so big, but I want to share my experience so people can understand the realities of working as a data scientist.

I have been working for more than half a decade as a data scientist, and I can say that I have done many things during the process. However, here are some key points of my activities that I want to share:

1. Collaboration with Cross-Functional Teams

As a data scientist, I know there are many technical expectations that I need to fulfil. However, I want to say that many of my daily jobs involve collaborating with other departments, either business or non-business. These include domain experts, business owners, data engineers, data governance, DevOps, etc.

Solving business problems with data science is a cross-team responsibility, so my work involves many people from various functions. However, I think it’s also because I am now in more of a senior position that I could be responsible for connecting with many functions. In my junior time, I did much more work on the hands-on part, which is my favourite.

2. Data Preprocessing, Exploration and Analysis

I would say that most of my daily work when I handle a project directly is to ensure the data I have is right and preprocess them to be ready for subsequent activities.

This involves using statistical methods to analyze data and uncover patterns, trends, and relationships. It also involves understanding what data I have before I clean them or create new features based on the available data. Understanding the data before going into more complex modelling is crucial.

I would say 70% of my project times would be in this phase.

3. Model Development and Validation

Yes, I am doing model development to solve the business problem. People usually think this when we do data science, although machine learning model development is just a part.

Building the model design and establishing the perfect model for solving business problems takes much more time than building the model itself. I don’t necessarily build models every day, but discussing how to improve them is what I do almost daily.

Beyond building models, I spend time tuning and validating models to ensure they perform well on unseen data. This involves selecting the right algorithms, adjusting parameters, and using techniques like cross-validation.

4. Machine Learning Operations (MLOps)

It’s become more important than ever to have a model in production that we need to maintain and monitor. I want to concur that I am still less experienced in this field as I have only more focus on the MLOps since last year. However, I have been involved in developing the AWS cloud since the beginning.

I know that MLOps might not be what many data scientists do, but we must be involved. This is our model, after all. This includes monitoring model performance, updating models with new data, and ensuring that the models continue to meet business needs.

5. Presenting Insights to Stakeholders

Data scientists often present their findings and recommendations to non-technical stakeholders, which I also constantly do. As a data scientist, I was expected to bridge the technical analysis into something that business can understand.

This involves storytelling with data to highlight key findings and their implications. This also involves a lot of visualization to make things easier for business.

6. Research and Development

Data science is an ever-growing field; we can’t always stay in one spot. No matter what business your company is running, we can always make improvements to solve the business problem. That’s why research and development of how to solve them is also a data scientist's responsibility.

I dedicated some time to reading academic papers and experimenting with emerging technologies to improve as a person and help the business. We data scientists need to have a mindset for continuous learning to succeed.

Career Growth and Opportunities

Those who want to consider data scientist as their career option must see the career growth and opportunities within. I can see that data scientists could have a straightforward career trajectory, but those options might not be the most exciting choices for some.

The career trajectory for a data scientist offers numerous paths for advancement and specialization, depending on the business needs as well. Here are some of my experiences and takes on where data scientists could grow.

1. Entry-Level to Senior Positions

Starting as a junior data scientist, individuals typically focus on honing their data analysis, model building, and coding skills, which I did. With experience, I gained expertise in more complex projects, leading to roles for senior data scientists, where responsibilities include leading projects, mentoring junior team members, and making strategic decisions based on data insights.

2. Specialization

This is where we could become more diverse, and I think where data scientist should make their choices. A data scientist career could follow T-shaped learning where we know the general but should focus on one thing to make us special.

We could specialize in specific areas such as machine learning, artificial intelligence, deep learning, natural language processing, or big data technologies. Specialization allows for a deeper understanding of certain techniques and tools, making us professionals highly sought after for projects requiring expert knowledge.

3. Leadership Roles

This is the other spectrum for data scientists. Some might want to aim for a job with more leadership roles, such as Lead Data Scientist, Data Science Manager, or even Chief Data Officer (CDO). Every company always needs leaders who can define data strategy for the organization and ensure that data-driven approaches align with business goals.

This is the career path where you might not be 100% hands-on with the project as you want to be more of an overseer than an individual contributor.

4. Transitioning to Related Fields

The skills acquired in data science are highly transferable, allowing us professionals to explore related fields like data engineering, data analytics, and machine learning engineering.

Each field offers a different focus, from the design and management of data infrastructure to the more detailed data analysis for business insights or the engineering aspects of deploying machine learning models into production. It’s like specialization, but you might need further data science knowledge.

5. Consulting and Freelancing

For those who prefer variety and flexibility, consulting or freelancing as a data scientist can offer the opportunity to work on diverse projects across different industries.

This path allows for exploring various data challenges and solutions, often with the potential for higher earnings and the flexibility to choose projects that align with one's interests and expertise.

You can also try creating content like I did. The job might have less certainty regarding income, but it offers more flexibility.

Work-Life Balance and Financial

Speaking of income, let me explain my take on the work-life balance and financial situation as a data scientist.

While the profession offers intellectually stimulating challenges and the opportunity to make impactful decisions based on data, it also demands a high level of dedication and time management to maintain a healthy balance between professional responsibilities and personal life.

1. The Reality of Work Hours

Data science projects often come with tight deadlines and high expectations, which often happens in my experience. This can lead to periods of intense work, where long hours and the occasional need to work outside of standard office hours become necessary to meet project goals.

However, this is not a constant occurrence and can vary greatly depending on the project. However, you must expect more over time, especially as an individual contributor.

2. Stress Levels

The complexity of work in data science can be a double-edged sword. On one hand, solving complex problems and finding innovative solutions can be highly rewarding. On the other, it can also be a source of stress, particularly when dealing with ambiguous problems or the pressure to deliver actionable insights within short timeframes.

Effective time management, realistic expectations, and seeking support when needed are necessary for a data scientist. I try to minimize my stress by doing what I like.

3. Finding Balance

Many organizations know the demands placed on data scientists and strive to create a supportive work environment. This includes flexible working arrangements, such as remote work options, flexible hours, and generous leave policies, to help employees manage their personal and professional lives more effectively.

For myself, my current companies have great flexibility for remote opportunities, which I find a pulling point for leaving. If remote jobs are one of the things you must have, then a data scientist job might also be suitable for you.

4. Financial Considerations

The financial situation in data science science is often cited as one of the major attractions of the field. With high demand across various industries, data scientists typically command competitive salaries, even at entry-level positions.

I am paid quite well for my position as a data scientist for my country; however, I know I am pretty much underpaid if I am considering the whole world. But isn’t that why we keep improving for a better financial situation?

I still think data science's financial situation is better than most jobs. Of course, you might feel jealous of some who make big money, but I would keep reminding you that a data career is one of the most lucrative jobs.

Honest Review Final Take

Navigating a career in data science combines the challenge of mastering technical skills with the satisfaction of solving meaningful problems, emphasized by the lure of financial reward.

From my biology to data science path, I've learned the importance of continuous learning, effective networking, and adapting to new challenges. The work—a mix of collaboration, data analysis, and model development—demands technical knowledge and the ability to process complex insights for non-technical audiences. Opportunities in data science are vast, ranging from specialized technical roles to leadership and consulting, each offering its own set of rewards and challenges.

Yet, the journey through data science is about more than just professional success; it's about maintaining a balance that nurtures personal well-being alongside career growth. The profession's high demands require a supportive work environment that values flexibility and work-life harmony. While the financial benefits of data science are significant, they come with expectations that must be carefully balanced against the need for personal fulfilment and health.

Achieving success in data science thus means finding a path that allows for both a professional and a satisfying personal life. It’s a career that I would keep choosing, but some might not enjoy it, which is fine.

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