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For a non-specialist, it’s difficult to tell the difference between a data scientist and an actuary because their job descriptions involve in-depth data processing. However, they are distinct career paths, and each exists as an independent specialty. For someone thinking of building a career in this direction, is there one of these two (data science and actuary) better than the other?

Data science is a field that creates new algorithms to make sense of data. Actuarial science works with pre-developed software programs to analyze data and determine potential risks. They both have an excellent affinity for data, so the skill set required for both tends to intersect.

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Both data science and actuary are highly desirable career paths, but the choice of what is better for you is dependent on the requirements for each of them. Therefore, it’s essential to understand what data scientists and actuaries do and the skill set needed for each of them. This article sheds light on both specialties so that you can make an informed decision when choosing.

What does a data scientist do?

Data scientists collect data, analyze it, and make statistical-based predictions to generate solutions for complex problems. They work closely with key stakeholders in a business model to determine how to use data to meet set goals.

They combine their domain and programming expertise to create data modeling processes and algorithms that effectively extract the required data.

They also use relevant software tools and their statistical knowledge to analyze the data and translate it into usable information for the business.

To become a data scientist, there are several skills you should master as they play a massive role in the field. They include:

  1. Machine Learning – You’ll be required to enter algorithms and implement statistical models for the computer to learn and break down the data automatically. Without a proper mastery of machine learning, you’ll encounter challenges executing these models.
  2. Statistical Analysis – Do you have a keen eye for patterns? You’ll need to detect data patterns and anomalies in the data you’re analyzing. A tight mathematical background in statistics and calculus is a big plus.
  3. Computer Science – This skill will ease your human/computer interaction, conduct numerical analysis, use software and database systems, and effectively employ artificial intelligence principles.
  4. Programming – Proficiency in writing codes in different languages like Python, Java, SQL, etc., as you’ll be required to create programs that can analyze unstructured data to solve complex problems.
  5. Data Storytelling – After analyzing the data, you’ll be required to explain this data to a non-technical team that’ll implement it to bring the desired results to the business. Your ability to do this will significantly determine the results you get from the data.

These technical skills are the most sought after when companies are hiring data scientists. However, other soft skills like analytical thinking, curiosity, critical thinking, and a strong business intuition make a data scientist stand out and become indispensable.

Every project a data scientist handles is unique, but the process of data analysis follows a similar approach.

It starts with one asking the right questions to collect the required data. It’s then processed, integrated, and stored, awaiting investigation and analysis.

Appropriate models and algorithms are developed to analyze it with the help of practical data science techniques like machine learning and artificial intelligence.

The results are adjusted and improved before submitting to the stakeholders, who either approve for implementation or suggest adjustments.

After solving one problem, the process is repeated to solve the following problem.

What does an actuary do?

The uncertainty of the future and potential risks create jobs for actuaries.

Actuaries use numbers to evaluate the likelihood of an undesirable event in the future that could ruin business and design creative ways to either avoid the risk or reduce its impact on the business.

In more explicit terms, actuaries apply statistical and mathematical analysis of data to evaluate potential risk in a business model. They are commonly present in firms that deal with wealth management, insurance, banking, investments, health financing, etc.

New institutions dealing with data analytics and require data-based decision-making for risk management also seek services from an actuary.

With enough data and relevant statistical data processing software tools, an actuary assesses the risk and advises on actions to take to evade the risk.

Actuaries are primarily helpful in the insurance industry as they develop policies and insurance premiums and the charges for each policy.

They ensure that the premiums are profitable for the insurance company while remaining competitive with other companies.

In the insurance field, actuaries can specialize as health insurance actuaries, life insurance actuaries, property and casualty insurance actuaries, pension and retirement benefits actuaries, and enterprise risk actuaries.

For instance, a pension and retirements benefits actuary will help companies establish favorable retirement plans for their consumers, ensuring that both the insurer and the consumer are satisfied with the terms laid out.

An actuary can rise to the level of a consultant, especially those working in the public sector and the government.

The skill requirements for an actuary include:

  1. Computer Skills – In-depth computing knowledge is an essential skill because you’ll use computer programs and software tools to process the data. You are also supposed to be in a position to share data across different platforms without altering it seamlessly.
  2. Mathematical and Statistical Analysis – You must be familiar with mathematical subjects like calculus, probability, statistics, and accounting to correctly analyze data and identify patterns and trends in the data set.
  3. Problem-solving – As an actuary, you should intuitively identify a solution to a problem using the knowledge extracted from the data and manage potential risks.
  4. Communication skills – You must effectively communicate your findings, explaining the causes of risk in the business and the proposals you have to lessen the impact of the risk.

Some of the responsibilities of an actuary are:

  • To gather and compile data for statistical analysis.
  • To estimate risks based on probability and the likely impact of unexpected events such as natural disasters, death, accidents, etc.
  • To test and recommend/apply policies that help mitigate the risk and maximize profits for the business.
  • To generate reports in illustration tables and charts to explain their findings and proposals.

Data Scientist vs. Actuary: Salary

According to the U.S. Bureau of Labor Statistics (BLS), the median annual salary for an actuary with an entry-level Bachelor’s degree is about $111,030.

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If broken down according to the industries they work in, an actuary in the scientific, professional, and technical sectors would make about $113,780. One in the financial and insurance sector would make $112,800, in the government, $110,430, and in the management of companies and enterprises, $99,550.

For a data scientist, the Occupational Employment and Wage Statistics (OEWS) report shows a median salary of $98,230 as of May 2020. Data scientists work in a wider scope as their coverage is broader. This means that the wages differ, with top-paying industries where data scientists work paying as high as $144, 090 annually.

The salaries also differ according to states, job descriptions, and bonuses offered in specific companies.

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Data Scientists Vs. Actuary: Job Demand

BLS predicts a growth rate of 18% between 2019 and 2029 in the employment of actuaries as more actuaries will be needed to aid in developing, pricing, and evaluating various insurance premiums and estimate potential costs for the new risks.

Companies will need actuaries to manage enterprise risk and help them avoid and manage financial risks in their business. As a result, the companies should be able to implement investment strategies that bring about economic returns.

As more people adopt property and health insurance, insurance companies will need more actuaries to handle the extensive data from customers and use it to make accurate and reliable predictions of future risks.

Within this estimated period, job opportunities for actuaries are expected to remain highly competitive for entry-level actuaries considering the increased number of graduates every year.

Data scientists are looking at a projected growth of 15% in the period between 2019 and 2029 as demand for better technology to analyze large data increases.

Businesses are collecting more data daily, and so the demand for advanced data-mining services is on the rise.

Data scientists, especially those specializing in computer and information technology, will be required to create algorithms and software tools that will help break down large data.

Opportunities for data scientists are inexhaustible as almost every sector requires a data analyst. Entry-level positions for data scientists are also expected to experience high competition as more people are choosing data science as their career path.

Will Actuaries be replaced by data scientists?

The skillset and responsibilities for both actuaries and data scientists tend to relate in many ways, but it’s unlikely that data science will replace actuary because they both have their place in the industry.

However, it’s possible for data science to have the upper hand as they continue to develop AI software tools that can imitate human instinct. Machine learning is gradually taking over human tasks to meet the needs of exponentially growing data.

While these two specialties appear to be in a competition, many people are choosing data science as it is perceived to be more versatile and is acceptable across many industries. Certified data scientists can then choose to specialize in actuary later in their career progression.

The SOA has also been recently updating its actuarial science curriculum to introduce actuaries to the core and basic data science skills. The integration could mean erosion in the distinction of both specialties, but that is not in the near future.

It’s hard to replace actuaries because of their unique skills, which have been cultivated over the years. There’s no room for a shortcut in the journey of becoming an actuary as the practice requires a deep understanding of regulatory and insurance economics.

They incorporate the skills of interpretation, translation, judgment, communication, and specific insurance knowledge to the data analysis process and provide real value to it. Machine programs only run data against other multiple data sets while looking for correlations, but an actuary will sift through the data, find better options to analyze it to get better recommendations.

The combination of professional standards, quantitative analysis skills, and the knowledge of insurance and regulations actuaries have are unique characteristics that machines cannot imitate.

As the industry shifts towards digital systems and automation, the possibility of merging the two disciplines tends to increase.

Actuaries are challenged with learning new techniques to handle more complex data models and increase their adaptability and flexibility.

Advanced models will call for superior business judgment and excellent communication skills to accurately translate results into profitable ratings and pricing.

If both disciplines merge in the future, the industries will be expected to engage both data scientists and actuaries to work in tandem and in similar roles.

Conclusion

Both data science and actuary are promising career paths that can lead you to a fulfilling career life.

However, to know which one is best for you, it’s important to take time to evaluate the skill set needed for each and the responsibilities involved, as discussed in this article.