Data science at Oracle: A thriving career path

In today’s digitally driven world, data is the lifeblood of businesses and organizations. The ability to gather, analyze, and interpret it into data-informed insights makes data scientists crucial. And it isn’t just a single profession—it’s a dynamic and highly sought-after field that welcomes all sorts of people with all sorts of skillsets.

What is data science?

Have you ever wondered why businesses like Oracle make the decisions they do? Data science is the practice of transforming raw data into actionable insights like technology solutions. It combines statistical analysis, computer science, and specific industry expertise to uncover patterns, make predictions, drive informed decision-making, and build better language models among other things.

Data scientists come from diverse backgrounds from social science to fintech, but they all work with massive datasets and use techniques like machine learning (ML) and data mining, to reveal valuable information.

Oracle Principal UX Data Scientist, Ping Mamiya Chao is a former academic research scientist who transitioned to industry because she loves to build tools that enable people to live better.

“My work in data science is really inspired by my previous research background in MRI brain imaging, and genetics sequencing in human language learning,” she shares.

Turning data into results

Now, she uses those skills to help different teams around the business understand what customers need to thrive. She’s passionate about the transformation of data into real results—a quality she shares with other in the profession.

Principal Data Scientist, John Peach sees a lot of comparisons with engineering disciplines.

“Civil engineers often specialize in various plumbing systems. Some primarily work on multi-unit residential buildings. Others design the plumbing of large commercial buildings—data science also consists of several specialized areas that have different roles, skillsets and outcomes.”

“They could be split up by the industry they work in, the type of data they work with, the type of problems they address, or the type of skills that they need.”

A day in the life of a data scientist

Staff data scientists wear many hats, making their daily routine diverse and exciting. Ping sees it as forming a bigger picture from a collage of tiny details.

“We do various things. We try to identify the pattern from really big data. So, for example in retail, you do online shopping, or you’ll look at house pricings in real estate, or if you want a forecast of stock price in the next three years… All this analysis is done by data scientists.”

“What we’re trying to do is to identify patterns in data. Trying to distill very unstructured data and make it to an understandable format so we can see that if we can extract any pattern or we can identify any relationship among various factors,” she explains.

The potential here is incredible with practical applications from weather prediction to healthcare, and beyond.

Focus areas

Broadly, there are six main areas of focus in data science, and you could find yourself moving between several during a typical workday.

1. Data collection: Gathering data from multiple sources, which could range from structured databases to unstructured text and images.
2. Data cleaning: Cleaning, formatting, and preprocessing the data to ensure accuracy and consistency.
3. Exploratory data analysis (EDA): Visualizing and analyzing data to identify trends, anomalies, and patterns.
4. Predictive model building: Developing predictive models using ML algorithms.
5. Model performance evaluation: Assessing model performance and fine-tuning them for better accuracy.
6. Communication: Presenting findings to non-technical stakeholders in a clear and understandable way.
7. Continuous learning: Staying up-to-date with the latest technologies and methodologies.

Entering the field

Getting started in data science usually requires a foundation in mathematics, statistics, and programming (languages like Java and Python, C/C++, Scala, and Julia). Data scientists often have a bachelor’s degree in a related field, like computer science, statistics, or engineering with many having advanced masters or doctoral degrees.

Building a portfolio of data-related projects and being able to show practical experience through internships or personal projects is also useful.

Right now, certificates in areas like AI and ML can prove invaluable for both aspiring and seasoned data scientists wanting to develop their skills.

Making it count

The emphasis on mathematics can sometimes intimidate people considering entering data science. While having a head for numbers is valuable, a degree in mathematics isn’t strictly necessary. Seasoned principal data scientist Alireza Dibazar feels that it’s more about aptitude and willingness to learn rather than knowing all the answers right away.

“While a background in mathematics can be helpful,” he explains, “data scientists can develop their mathematical skills over time. What’s essential is a strong willingness to learn and a passion for problem-solving.”

As a member of our anomaly detection team, Alireza knows all about the novel thinking required in spotting and analyzing trends.

“Qualities of a successful data scientist include critical thinking, curiosity, communication skills, and the ability to work collaboratively. Experienced data scientists often come from diverse backgrounds such as computer science, physics, economics, engineering, and more.”

“The ability to apply their expertise to data science is what matters most.”

Career progression

Fang Tu, a senior principal data scientist in our AI Services department, encourages anyone starting out in data science to take the long view. It’s a field that rewards depth of knowledge and patience, and shortcuts are rare!

“All good things take time,” Fang emphasizes. “Just like any other career, think long term while focusing on the present, make steady progress day by day, that’s what’s going to lead you far.”

“With new technologies emerging at a fast pace in the ML field, it’s important to keep reading and exploring new things every day. There are tons of free resources online: tutorial videos, public datasets, open-sourced packages, on pretty much any ML applications you can think of. There’s never a dull moment!”

Qualified talent

Data science projects are incredibly diverse at Oracle and so are opportunities to grow. Generative AI and ML are of major interest across the tech industry at present, but statistical analysis, predictive modelling, NLP, recommendation systems, anomaly detection, speech, numerical models, language models, and image are also creating huge demand for qualified talent.

With the right education and dedication, you can embark on a successful career as a data scientist. The high demand, competitive salaries, and opportunity to work on meaningful projects make it an excellent career choice.

Do you want to kick-start your data science career, pivot into the field, or grow your skillset? Explore our open roles today and help create the future with us.