We are in a big data world today and data scientists are the backbone of this digital world. Customer action, consumer behavior, business operations, and decision-making at its very core are now governed by data points for enterprises. Customers leave data footprints via neural networks across several platforms and devices, thus generating data by the droves. Did you know that the creation of data globally is projected to grow by more than 180 zettabytes by 2025.
No wonder enterprises depend on data science professionals to derive insights from large data volumes! This has massively boosted data scientist careers.
The Demand, Supply, and Talent Gap
The ability to collect data has spawned businesses, products, and services that thrive on technologies like artificial intelligence (AI), predictive analysis, and machine learning (ML). The pandemic also helped the cause of data scientist careers. It is estimated that 55% of businesses have started using data analytics to improve efficiency as a result of COVID-19. It is only natural that these enterprises and industries demand talent skilled in data science.
Also, a recent LinkedIn report shows that data science specialists, ML engineers, and AI specialists are some of the top 15 in-demand and fastest-growing jobs right now. The reason? There is a huge gap in the demand for data scientists and their supply! As per a Times of India survey conducted in 2021, 92% hiring managers feel that there is a shortage of talent when it comes to data science.
How many different career paths can a data scientist follow?
We already know that fat paychecks and abundant job opportunities are perks that come with pursuing a data science. However, did you know that a data scientist must fulfill many different kinds of roles? A data scientist’s work is not like a glove that fits every hand; the profiles within this domain are often nuanced requiring either skillset or experience in certain areas. Data scientists can work as coders, scientists, strategists, and more.
You’re never just a data engineer, programmer, analyst, or architect. These roles are often overlapping and agile in terms of their scope. Several data science functions work together to build frameworks, develop software, and create ML algorithms. Data science offers professionals opportunities to move into leadership roles, consultant roles, and analyst roles in various industries.
One look at the data scientist salary comparison shows that an entry-level data scientist with less than a year’s experience starts at US $85,096 per year. As years of experience add on, salaries can go as high as US $135,961.
Junior/Entry-level Roles in Data Science (Experience: 1-4 years)
#1: Data Science Intern
Entering into a data science career path means possessing excellent SQL, R, and Python programming skills. Assuming you have a deft hand at using these programming languages, the best place to start would be to join as an intern and entry-level data scientist in an industry of your choice.
#2: Junior Data Scientists
Junior data scientists work on volumes of raw data early on in the processes. Collecting, organizing, validating, cleaning, and structuring data using code to further pass on to senior data scientists in the organization. At this level, aspiring data scientists are exposed to most data job functions, while honing programming skills and applied mathematics knowledge.
Years of experience and continued learning enables you to branch out as senior data engineers, senior data architects, ML engineers, business analysts, or continue to progress the data scientist’s career path.
#3: Business Intelligence Developer and Market Analysts
Entry-level data professionals are also given the job titles of Business Intelligence (BI) developers and market analysts. At an entry-level, BI developers work actively with various departments on real-time industry and company data. They must identify problems, market trends, evaluate business processes, and uncover valuable business insights. Thus, they need to be experts at data modeling and visualization tools like advanced excel to report findings.
A graduate with a business administration degree and a penchant for data can easily start their careers in this profile. However, to branch out into data analytics, market analysts must learn database skills and have thorough knowledge of applied mathematics as well as statistics.
Mid-level Roles for Data Science (Experience: 4–8 years)
A minimum of three years of experience as junior data scientist enables you to choose the field to build your data science career in. Larger enterprises and tech-driven companies have robust data development teams that comprise senior data analysts, senior data scientists’, data engineers, data architects, as well as AI and ML engineers.
#1: Senior Data Engineers and Architects
Senior data engineers and senior data architects work together to build data infrastructure, software, and frameworks to collect and manage terabytes of raw data. This data could be coming from varying sources into a centralized location. They are key to ensuring flawless data flow, modeling, and architecture of how data is captured, database management, security of data, data integrity, and data governance based on business goals.
#2: Senior Data Analysts
Senior data analysts are also business and market analysts with advanced expertise and business acumen. Organizations across industries rely on data analysts to collaborate with various departments to analyze big data, historical data, running A/B testing, and working with data visualization tools such as Tableau. Their reports and data analysis help C-suite leaders drive more revenue, find growth opportunities, and solve business problems.
#3: Senior Data Scientists and Data Science Managers
A senior data scientist’s work involves:
- Identifying trends and patterns across big data
- Building ETL pipelines for logical flow of data
- Leveraging deep learning
- Using ML to forecast data
- Graphically representing trends to various stakeholders
- Driving actionable insights
Senior data scientists build frameworks, write codes for businesses to automate processes, and deliver enhanced consumer experiences.
Take, for example, Uber’s data science workbench (DSW). Uber’s data development team worked extensively on developing an all-in-one toolbox that is today accessed by data scientists, engineers, and operations teams across the organization. More than 4000 active users leverage DSW for complex applications, such as pricing, safety, fraud detection, and customer support, among other foundational elements of the trip experience. It centralized everything required to perform data preparation, ad-hoc analyses, model prototyping, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface.
Directorial Roles in Data Science (Experience: 8 – 12 years)
To progress from a senior data scientist’s role to the next level, professionals need to add advanced data science certifications and data science programs to their skill sets. Moreover, the job titles available with close to 12 years of expertise are lead data scientists, principal data scientists, and data science directors. Expect the average base salary at this stage to rise up to US $155,166 per year.
Senior data analysts, however, switch roles laterally or move up to job roles such as senior business analytics managers, business insight and analytics managers, director of analytics product managers, and some even move into specific data scientist roles. So, much would depend on the industries one has worked in and the scope of the projects one has handled. Salaries for such roles range from US$102,000 to US$124,000 per year.
Machine Learning (ML) Engineers
Several data scientists evolve into ML engineers. Many engineers, data scientists and programmers do start their careers as ML engineers. However, the role is symbiotic. To develop holistically as a professional in ML, one needs years of experience. ML and AI fields are growing exponentially as more and more organizations depend on automation to acquire customers, retain them, and deliver customer experiences. Data scientists with expert skills in C++, Natural Language Processing (NLP), deep learning as well as software engineering ascend to become senior ML engineers and lead projects.
At this stage lead ML engineers supervise teams to build products while developing ML/AI systems and software with advanced algorithms. As an ML engineer, one is expected to build robust platforms like Amazon and Netflix! About 35% of what consumers purchase on Amazon and 75% of what customers watch on Netflix come from product recommendations based on highly personalized algorithms. In fact, ML models and tools enabled the Amazon team to eliminate 915,000 tons of packaging material worldwide, and reduced the use of boxes from 69% to 42% in 2020.
Advanced Roles in Data Science (Experience: 12 – 16 years and more)
Heading an enterprise’s data science division requires strong skills in NLP, ML, BI, people management, and big data analytics. Chief Data Officer (CDO) and head of data sciences is the most advanced job title in a data scientist’s career path. As per market trends, 59% of chief data officers report to a business leader. Also, 80% of the top stakeholders’ chief data officers support are businesspeople. This includes the CEO, COO, CFO, and head of digital transformation.
We wish we could say that with 12+ years of experience, you have officially mastered the data science domain. But, the dynamic nature of technology ensures there is always something new to learn and master. Experience in a number of years does prepare a data scientist to lead and supervise pioneering tech organizations and enterprises. But the quality and depth of that experience is vital.
Data science is an evolving technological field and professionals need to constantly upskill and reskill to stay relevant. They must evolve with the business and its challenges. They must also equip themselves with the latest technologies and languages to solve critical business problems.
Emeritus offers exhaustive line-ups of online data science and analytics courses that equip professionals with concepts and applications of business analytics, data visualization, ML, and AI from top universities across the globe.
By Janice Godinho
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