Know the Complete Journey of a Senior Data Scientist

Between 2018 and 2028, the career is expected to grow 16% and produce 5,200 job opportunities across the U.S.

~Zippia

Data science is a rapidly-booming area in the current world. Many industries are utilizing data science in some shape or form. This resulted in the demand for data professionals who possess skills to solve the issues related to data and help drive the business to success.

The data science professionals play a vital role, as they scrutinize and leverage the business’s dataset to enhance the business’ capabilities for accomplishing the end-to-end objectives of the business. The senior data professional is instrumental in offering the business to continue its evolution into an analytical and data-driven culture.

In this article, let’s dive deeper about a few elements of what it means to be a senior data scientist.

Requirements to be a senior data scientist

Senior data professionals take help from data to shape the direction in which the business grow. They direct and employ the efforts of junior staff as they spearhead several data-driven projects. Following are the few requirements to be a data scientist at the senior level:

Education

A bachelor’s degree in statistics or machine learning or mathematics or computer science or economics or any other related quantitative field.

Experience

Extensive experience as a data scientist in any industry i.e., at least 5 years of working experience.

. Essential skills to be a senior data scientist

The senior data science professionals mainly work with business stakeholders to know their objectives and to determine how data can be used to achieve those goals. To accomplish all these they require to master a few essential skills that they need to do their work on a daily basis:

Technical skills

. Programming — Writing computer programs and analyze massive datasets to get the answers to complex problems. They are needed to write code working in languages like Java, R, Python, and SQL.

. Statistical analysis — Identify the patterns in data, that also include a good knowledge of anomaly and pattern detection.

. Machine learning — Applying algorithms and statistical models so that the computer automatically learns from the given data.

. Computer science — Apply the concepts of database systems, AI, numerical analysis, software engineering, human and computer interaction.

. Data storytelling — Communicate actionable insights using data, often for the stakeholders and non-technical audience.

. Non-technical skills

Apart from the technical skills, to be a senior data scientist one also requires soft skills, as they play a vital role in helping companies to make goal-oriented decisions. These are the following soft skills:

. Interpersonal skills — Communicate with a diverse audience across all levels of a company.

. Business intuition — They also connect with the stakeholders to get a better understanding of the issues they want to solve.

. Critical thinking — Application of objective analysis to the facts before coming to a conclusion.

. Analytical thinking — Seeking better analytical solutions to abstract business problems.

. Inquisitiveness — Understanding beyond what’s on the surface discovering patterns and solutions within the data.

Doing a data science certification is highly advisable to learn the required skillset to be in a senior level. The certifications are usually considered as one of the main aspect to level up in one’s career.

Responsibilities of a senior data scientist

The data scientist in the senior level as compared to a non-senior data scientist is that they provide many advanced expertise on several areas that are very much required for the company’s growth. There are several important responsibilities that are part of their job role.

1. Staying updated about the latest advancements of data science and adjacent fields to ensure that there are good results.
2. Suggesting, and managing data-driven projects that are valuable for a business’s interests.
3. Collating and cleaning data from various entities so that it can be helpful by junior data scientists.
4. Formulating creative ideas for leveraging the business’s vast collection of data in the databases.
5. Monitoring the performance of junior data scientists and giving them required practical guidance.
6. Managing the activities of the junior data scientists, and making sure that they properly execute their job responsibilities, which should be aligned with the business’s vision and objectives.
7. Finding and applying advanced statistical procedures to obtain actionable insights.
8. Collaboratively working with junior data scientists for building the latest and improved analytics systems such as from prototyping to production.
9. Cross-validating models and delegating works to junior data scientists to get better outcomes and also for completing the projects on time.
10. Producing and disseminating non-technical reports, which detail the accomplishments and limitations of every project.

Top countries that pay the best salaries for big data scientist

The data science professionals who are in senior level get attractive salaries. These salaries usually differ by geography. North America pays typically higher than Europe. Following is a list of top countries that pay the highest salaries, along with the median salaries are mentioned in US$.

Additionally, as senior data professionals get experience and relevant big data certifications, they often shift to more senior positions with better and higher pay. These include:

. Data Science Manager: US$135,401 per year

. Data Science Director: US$157,273 per year

End Notes

Data is currently the most essential tool in any industry. Almost all companies need expert professionals who with their expertise will add and better more value to their business and work towards its growth. Getting certified will ensure to be a part of a highly desired talent pool.