When I first started as a data scientist, I was confused by the various types of data science positions and their responsibilities. When I wasn't even clear about what I was doing, I didn't want to apply for a job. You might also be confused by the roles of all the data science there and the explanations of those subtle jobs. Which roles match a particular skill set? How do you know what you're working on?
Let's take a look at some of the most popular data science roles and what they actually do.
Top Data Science Job Title
- Data Scientist
- Data Analyst
- Data Engineer
- Data Architect
- Data Storyteller
- Machine Learning Scientist
https://www.youtube.com/watch?v=vrdnbxx8bi
1. Data Scientist
As a data scientist, you address every aspect of your project, from knowing what's important to your business, to collecting and analyzing your data, and finally visualizing and presenting your data.
A data scientist is the jack of all transactions. The result is to reveal larger patterns and trends in the data, while providing insights into the best solutions for a particular project. Additionally, companies often request data scientists to research and develop new algorithms and approaches.
In large companies, team leads are often data scientists. The skill set allows you to oversee other professionally skilled employees while leading the project from start to finish.
2. Data Analyst
When looking for a job, you can also come across the role of a data analyst. Data science and data analysis sometimes overlap. In fact, if most of the work you actually do is data analytics, companies may hire you as a “data scientist.”
Data analysts are responsible for a variety of tasks, including visualizing, transforming, and manipulating data. You may also be responsible for web analytics tracking and A/B test analytics.
Data analysts are responsible for visualization, and are often responsible for preparing the data for business communication. Analysts prepare reports that effectively show trends and insights gathered from the analysis in a way that non-experts can understand.
3. Data Engineer
Data engineers are responsible for designing, building and maintaining data pipelines. They test the company's ecosystem and prepare them for data scientists to run the algorithms. Data engineers also work on batching the collected data, matching the format to the stored data. Finally, engineers keep their ecosystems and pipelines optimized and efficient, making them available to them so they can be used at any time.
4. Data Architect
Data Architects share common responsibilities with data engineers. Both require that the data be well-organized and accessible to data scientists and analysts, and improve the performance of the data pipeline.
Additionally, data architects design and create new database systems that match the requirements of a particular business model. The architect must maintain these database systems both functionally and administratively. In other words, the architect tracks the data and decides who can view, use and manipulate different sections of the data.
5. Data Storyteller
Data storytelling is often confused with data visualization. Data storytelling is not just about creating reports to visualize data and share statistics. It is about finding stories that best explain the data and develop creative ways to express that story.
Data storytelling spans the boundaries between pure, raw data analysis and human-centered communication. Data Storytellers need to capture the data, focus on specific aspects of the data, analyze its behavior, and use their own insights to create compelling stories that will allow a wide range of viewers to better understand a particular phenomenon. This position provides great value for the team and creates opportunities for data scientists to bending their creative muscles.
6. Machine Learning Scientist
Machine learning scientists are studying new approaches to data manipulation to design new algorithms. They are often part of the R&D (Research and Development) department, and their work usually leads to published research papers. Machine learning scientists usually work in academia rather than in industry. You can also look at machine learning scientists called research scientists or research engineers.
7. Machine Learning Engineer
Machine learning engineers are in high demand. They should be familiar with a variety of machine learning algorithms, such as clustering, classification, and classification, while still being up to date with the latest research advancements in the field.
Machine learning engineers must have strong statistics and programming skills along with basic knowledge of software engineering. In addition to designing and building machine learning systems, machine learning engineers must perform tests while monitoring the performance and functionality of various systems.
8. Business Intelligence Developer
Business Intelligence (BI) developers design strategies that allow businesses to find the information they need to make decisions quickly and efficiently. To do this, BI developers need to use new BI tools and design custom tools that provide analytics and business insights.
Since most of the work of a BI developer is business-oriented, you need to at least have a basic understanding of the fundamentals of business strategy and the internal and external aspects of the company's business model.
9. Database Administrator
While many companies design database systems based on specific business requirements, the companies that purchase the product actually manage the systems. In such cases, the company hires people (or teams) to manage the database. Database administrators monitor the database to ensure that data flow works properly and track data flow while creating backups and recovery. Managers also oversee security by granting employees various permissions based on job requirements and employment levels.
10. Statistician
Statisticians and data scientists have overlapping responsibilities, but there are important differences in how they play their role. Data scientists operate in a wider range of fields, including machine learning, software engineering, and automation. Statisticians, on the other hand, focus on using statistical models and mathematical concepts to identify quantitative relationships between data and solve problems.
11. Data Privacy Officer
With the rise in data privacy laws, Data Privacy Officers (DPOs) have become an important role for many businesses. To ensure that businesses are compliant with regulations, DPOs work with departments and leadership to design data protection strategies, develop best practices for advocacy for personal information, and evaluate the company's digital assets to resolve data-related privacy risks.
12. AI Ethics Officer
AI Ethics develops guidelines and values that organizations can follow to design and deploy AI in a safe and legal way. They translate these values into concrete actions by writing company policies and ensuring that all personnel comply with these rules.
Working with data engineers, data scientists, machine learning engineers and other team members, these executives can use accurate data to avoid algorithm bias and receive consumer permission before accessing personal data or other best practices.
What kind of jobs do Data Science do?
Common types of jobs in data science include data scientists, data analysts, and data engineers. Additionally, new roles such as machine learning engineers, machine learning scientists, and AI ethics are addressing the increasing use of AI and machine learning in the industry.
Is data science a good career?
Data science has promising prospects. According to the Bureau of Labor Statistics, the number of data scientists employed is expected to increase by 36% between 2023 and 2033. More recent roles such as data privacy officers, machine learning engineers, and AI ethics officers provide even more opportunities for professionals looking to enter the field of data science.
Is Data Science a high paying job?
Data science experts often pay six figures. According to the Bureau of Labor Statistics, the median annual sal for data scientists is $108,020. However, many factors such as experience and location can affect the amount that data science professionals are making.
