Data Scientist Job Description: Roles and Responsibilities

By Michael Warne 28-Apr-2023
Data Scientist Job Description: Roles and Responsibilities

A rather new field, data science, has become popular and significant thanks to today's data explosion. With the latest technologies collecting more and more data every second, businesses need experts to harness that data and use it to improve customer satisfaction and profitability. 

There are a few lingering questions, though. Who are data scientists, and what do they do? What are their skills? Most importantly, how can you become one?

In this blog, you will find the answer to all these questions and more. Without any delay, let us dive into the specifics of data scientists.

Who Is a Data Scientist?

According to India Today, the data science market can grow to 230.80 billion dollars by 2026. The market is growing at exponential rates. Data scientists use analytical, programming, and statistical skills to develop data-driven solutions and allow organizations to make informed decisions. 

Data scientists rule the world of big data. They sift the unstructured raw data businesses collect to find the precious information that can help them unlock new opportunities and boost their revenue. With the increase in data, the demand for data scientists has also exploded, making it a lucrative career option for people interested in the intricacies of data.

Data Scientist Roles and Responsibilities

A data scientist, as the name suggests, works with data. Their everyday responsibility includes working with data to analyze it and find solutions to different challenges. Data scientists use various statistical and analytical AI tools to interpret data and create predictive models to forecast trends and identify patterns. If you want to become a data scientist, it is crucial to understand their day-to-day responsibilities first. 

Here are some responsibilities you will find in a data scientist job description:

● Analyze and interpret data to identify trends and patterns

● Improve the data quality using machine learning techniques

● Evaluate advanced analytics tools and conduct data profiling

● Preprocess structured and unstructured data

● Develop predictive models

● Provide solutions to business challenges using data

● Cleanse and validate data collected by the organization

● Present findings in a clear and concise manner

● Coordinate with other departments and teams to implement new models

● Identify different data sources and ensure data integrity

● Enhance data collection procedures of an organization

● Stay updated with the latest happenings in the data science field

These are just some roles and responsibilities of a data scientist among the many things assigned, varying from organization to organization.

Skills Required to Become a Data Scientist

Organizations today rely heavily on the skills of a data scientist or a team of scientists to improve customer retention and satisfaction, boost revenue, and stay ahead of the competition. To be a successful data scientist, you must have the necessary technical and non-technical skills. Let us discuss them separately.

Technical Skills

Here are some technical skills you must have to be a data scientist.

Programming

One of the primary responsibilities of a data scientist is to organize unstructured data, something only possible through sound knowledge of programming languages like R and Python. They should also be skilled in database query languages like Hive and SQL. Knowing Java and C++, while not mandatory, can provide an additional advantage and help you stand out from the competition.

Analytical Tools

Data scientists regularly use various analytical tools. These include SAS, Hadoop, R, and Pig. Expertise in these tools is incredibly important for data scientists. Hadoop can help you with data exploration, filtration, and even summarization. Many employers today also prefer candidates with some expertise in these tools.

Machine Learning and AI

Machine learning and AI are useful in solving problems related to predictions of organizational goals. Machine learning techniques such as logistic regression, computer vision, natural language processing, and decision trees are especially useful for data scientists. AI and ML can help you work on data-driven models while also managing large data sets.

Data Visualization

Data scientists have to interpret and analyze the data first, then present it to stakeholders and decision-makers in a way they find visually appealing and easy to comprehend. Data scientists should also be skilled in data visualization. Data scientists must be familiar with visualization tools such as Tableau, Power BI, and QlikView.

Unstructured Data

One of the most crucial skills data scientists must possess is working with unstructured data. They should be able to comprehend unstructured data from different platforms and find insights that help make data-driven decisions.

Big Data

Many employers also ask for data scientists with skills in managing big data. Knowledge of special tools that help in handling big data can help you stand out from the competition while applying for a job. These include having familiarity with KNIME, Hadoop, Spark, and RapidMiner. 

Soft Skills

Technical skills without the essential non-technical skills can only take you so far in any career. Data scientists must have the necessary soft skills to succeed in their field. They must know how to talk to stakeholders and write easy-to-read and understandable reports. 

Data scientists must know how to take constructive client feedback, work on it, and explain it to fellow team members. They must master the skill of sitting in board meetings while signing contracts o let prospective clients know how they can help them grow their businesses and make them profitable.

Business Acumen

Successful data scientists must have relevant knowledge of the industry they are working in, and the business challenges their organization wants to solve. They must also be able to understand and prioritize business problems and leverage data to boost revenue. Only by possessing these skills can you take the right steps to find solutions.

Communication Skills

Translating their very technical findings in a non-technical way is one of the critical responsibilities of a data scientist. It only comes to people with excellent communication skills. You must be able to weave a story with your data to make it understandable for non-technical people. You must be able to highlight the crucial points succinctly and effectively. 

Teamwork

A data scientist collaborates with various departments and company executives to understand the business and its challenges. They have to assist in developing strategies and creating better solutions using data-backed insights. Teamwork is a skill you must possess as a data scientist. 

Data Intuition

As mentioned before, the data collected by organizations is vast. Even with all the sophisticated tools and technologies, not all valuable insights will be easily visible in large datasets, testing a data scientist for their intuitive skills. It helps you look beyond the surface and recognize valuable information. You can develop this skill with the right training and experience.

Problem-Solving

One of the most crucial soft skills for data scientists is problem-solving. A data scientist should be a natural problem solver with a penchant for solving complex challenges. You must think creatively and find solutions using the data and tools.

To be a successful data scientist, you must possess a combination of technical and soft skills. Here are the key skills required to excel in this field:

  • Strong programming skills: Data scientists must be proficient in programming languages such as Python, R, and SQL to collect, manipulate, and analyze data effectively.

  • Expertise in statistical analysis, machine learning, and data visualization: Data scientists should have a deep understanding of statistical analysis, machine learning algorithms, and data visualization tools to extract insights from data and communicate results effectively.

  • Knowledge of big data technologies: As data volumes continue to increase, data scientists need to be familiar with big data technologies such as Hadoop, Spark, and NoSQL databases to process and store large datasets.

  • Experience with data mining, data cleaning, and data preprocessing: Data scientists must have the skills to identify patterns and extract meaningful insights from raw data. This involves cleaning and preprocessing the data to ensure its quality and relevance.

  • Strong communication and collaboration skills: Data scientists must possess excellent communication skills to present their findings to stakeholders and collaborate effectively with cross-functional teams.

  • Industry-specific knowledge: Depending on the industry, data scientists may need to possess specialized knowledge of tools and techniques. For example, a data scientist in finance may need to be skilled in predictive modeling, while a data scientist in healthcare may need to be proficient in natural language processing.

Difference Between a Data Scientist and a Data Analyst

It is easy to confuse a data scientist and a data analyst. After all, both professions work with data. There are many differences between the two fields. Data scientists are a notch higher than data analysts in terms of academic proficiency as they develop their data-related questions. They also look for new ways to collect and analyze it.

On the other hand, data analysts use existing data and analyze it using the methods developed by data scientists. If you need clarification about which profession will suit you better, learn the responsibilities and skills of both to make the decision. 

If your interest is in analytics, then the data analyst profession may suit you better. If you are more into developing new techniques using various analytical and statistical tools, a career as a data scientist will be a better option.

Data Scientist Career Path and Salary

Data science is a highly lucrative career option, with companies all across the globe requiring data scientists. A report by India Today said that jobs in data science are growing at a rate of 14 percent. The same report also found that the market will create 11 million jobs by 2026. A data scientist with the right experience and skills can become a solutions architect, senior business analyst, and chief technology officer.

Data scientists earn more than other ITES jobs. According to AmbitionBox, data scientists make an average annual salary of Rs. 10 lakhs in India. The salary ranges between Rs. 4 lakhs to Rs. 25 lakhs. It increases with the increase in experience and skills. 

How to Become a Data Scientist?

Liked what you have read so far? Does a career in data science excite you? Here is how you can become a data scientist.

Get a Degree

The first step towards becoming a data scientist is to get your Bachelor’s Degree. Most data scientists have a degree in computer science, statistics, mathematics, or other relevant fields. Getting a Master’s Degree can also help you stand out from the competition.

Develop Relevant Skills

The next step is to develop relevant data scientist skills. You can do an online course or certification to learn programming languages, data visualization, and Hadoop. Work on developing the necessary non-technical skills.

Apply for an Entry-Level Job

It is always beneficial to get some experience. Look for entry-level positions in the data science field. You can also go for data analyst, data engineer, or statistician roles. These roles will enable you to work with data while helping you upskill yourself. Once you get some experience, you can start looking for data science roles.

Get Certifications

Another way of upskilling yourself and standing out from the competition is to get certifications. Below are details on some great data science certifications beneficial for your career.

Certifications for Data Scientists

Here are some of the best data science certifications to help you gain an edge while applying for jobs.

Certified Analytics Professional (CAP)

CAP is a vendor-neutral certification that validates your skills in understanding complex data, finding valuable insights, and presenting it to stakeholders in an easily understandable manner. You can take the exam only if you have three years of experience and a Master’s degree in a relevant field. 

If you have a Bachelor’s degree, you need five years of experience. You need at least seven years of experience if you have an unrelated degree. The certification is valid for three years. CAP certification can help you open doors to new and better opportunities.

Professional Certificate in Data Science (Harvard)

Harvard offers this certification through edX. It teaches the candidates about fundamental R programming skills, different statistical concepts, data visualization tools, and implementing machine learning algorithms, among other things. The certification has no prerequisites and helps develop fundamental skills necessary for data scientists.

IBM Data Professional Certificate

This certification is perfect for beginners. It covers different programming languages, tools, and libraries data scientists frequently use. It includes open source tools, Python, machine learning, databases and SQL, and data analysis. The course is self-paced, has no expiration date, and can be ideal for you if you are keen to launch your career in data science. 

Microsoft Certified: Azure Data Scientist Associate

This certification validates your skills in implementing data science and machine learning skills on Azure. The certification is proof of your expertise in creating a working environment for data science workloads and designing and preparing ML solutions. You need to clear the DP-100 exam to get this certification.

Other beneficial certifications for data scientists include the following:

● SAS Certified AI and Machine Learning Professional

● SAS Certified Data Scientist

● IBM Certified Data Engineer - Big Data

● AWS Certified Machine Learning - Specialty 

● Open Certified Data Scientist

● CDP Generalist Certification

● TensorFlow Development Certificate

Data scientists are essential everywhere. All industries, from healthcare to telecom, require data scientists to further their businesses and leverage data to survive and succeed in the competition. Becoming a data scientist takes work. It is not impossible, though, but not easy either by any means. With the right education, skills, and dedication, you can also enter this lucrative field and succeed. The certifications we have listed above are incredibly beneficial to begin and further your career in data science.

In summary, data scientists need to possess a wide range of technical and soft skills to excel in their field. By developing these skills, data scientists can effectively analyze data and provide valuable insights to businesses and organizations.

Michael Warne

Michael Warne is a tech blogger and IT Certification Trainer at Koenig Solutions. She has an experience of 5 years in the industry, and has worked for top-notch IT companies. She is an IT career consultant for students who pursue various types of IT certifications.

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