Everyone has it's own description of thoses postions. Even in the same company you will not have a unified description.
My goal with this page is to describe what are, in my opinion, the scope of those positions. This will help you understand better where I stand and what I can do for your company.
The other important use of this page is to make sure that we understand each other, because complex words or buzz words do not to get the job done, understanding each other do.
A data analyst extracts insights from data through thorough analysis. They identify trends and patterns according to business needs and requirements.
They are proficient in SQL and other tools such as Tableau, PowerBI, and Excel, which they use to create understandable reports for the business.
Additionally, they possess knowledge of structuring data warehouses and data marts using star and snowflake schemas.
Although I have some experience in this role, albeit not a lot, I am seeking opportunities that align more closely with my interests and skills. Specifically, I prefer roles that focus on data experimentation and cleaning, rather than creating reports in PowerBI.
In my experience, data analysts often spend the majority of their time on PowerBI reports and only a small portion on structuring data schemas, which leaves little room for in-depth data work.
I thrive on the hands-on aspects of data analysis and prefer to minimize time spent in business meetings.
A data engineer extracts raw data from databases and transforms it into a structured or semi-structured form before (up)loading it for use by data analysts and scientists. This process is known as ETL (Extract, Transform, Load).
Data engineers also work with the business, albeit in a less direct manner. Their job is to understand the needs of data analysts (and thereby the business) and to extract the necessary data from either the available data or another ETL pipeline.
Developing, maintaining, testing and optimizing data pipelines and datasets takes up most of their time. They are the hidden gems making data exploitable for everyone in the company.
I enjoyed working as a data engineer, as I have always loved scripting, automating thankless tasks, and saving time with those scripts, which were things I could do in this position. However, I began to feel complacent; I was doing my job without striving to learn more. Additionally, I have always been drawn to AI, which I couldn't work with in this role. This felt like moving away from AI and halting my career progression. These factors motivated me to shift my focus to data science, where I can explore data more deeply and maintain the passion that will drive my career forward.
A data scientist leverages advanced analytics and machine learning techniques to derive insights and solve complex problems using data. They possess expertise in statistical analysis, programming, and data manipulation, utilizing tools such as Python, R, and SQL. While data scientists share similarities with data analysts in their ability to explore data and communicate findings, their role extends beyond descriptive and diagnostic analytics.
In addition to analyzing historical data, data scientists develop predictive models to forecast future outcomes and trends. For example, they may create models to predict customer behavior, such as churn prediction or demand forecasting, enabling businesses to proactively address potential challenges. This predictive insight empowers organizations to make informed decisions and optimize processes across various domains.
The key distinction between data scientists and data analysts lies in their focus on predictive analysis versus descriptive and diagnostic analytics. Whereas a data analyst primarily works with existing data to address current business needs, a data scientist is equipped to anticipate future scenarios and develop strategies to mitigate risks and capitalize on opportunities.
For instance, consider a scenario where a company wants to predict if customers will change their stores in the future. They would require a data scientist to develop a predictive model, which can forecast the likelihood of a customer switching stores based on historical data and relevant variables. This predictive insight can then be used by the business to proactively intervene and retain at-risk customers, a task that goes beyond the scope of traditional data analysis.
Data scientists also possess a diverse skill set that extends beyond traditional data analysis. They can harness technologies like computer vision to extract insights from visual data, such as reading license plates or analyzing CCTV footage for security purposes. Additionally, data scientists can develop chatbots using natural language processing (NLP) techniques, enhancing customer service or streamlining internal processes with a LLM reading the thousands of pages of documentation for you.
Moreover, data scientists are proficient in acquiring and cleaning data, employing techniques like web scraping and Python scripting. Their versatility and expertise make them invaluable assets to any business intelligence team, serving as the Swiss army knife capable of tackling a wide range of analytical challenges.
As you have probably understood from everything above, I work as a data scientist. If the scope of the opportunity you had in mind for me falls within the description of a data scientist that I provided, please do not hesitate to contact me on LinkedIn.
