Do you want a job as a data scientist at Oracle? Don't worry! we will provide a clear roadmap that many ninjas follow to bag offers from top companies like Oracle.
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This article will discuss the prerequisites and strategy required to become a Data Scientist at Oracle.
But before jumping into the preparation strategy for becoming a data scientist at Oracle, let's learn some interesting facts about Oracle. So, let’s get started.
About Oracle👀
Oracle is the world's leading provider of business software. Oracle is one of the world's major technological corporations, operating in more than 175 nations. You might not be aware that Oracle is driving the cloud revolution. Oracle utilizes cutting-edge technology like blockchain, AI, and machine learning to address pressing real-world issues. The work Oracle does is not only changing business, but also assisting governments, empowering organizations, and equipping billions of people with the skills they need to keep up with change and make a difference. Examples include improving energy efficiency and reinventing online commerce.
Who a Data Scientist is🤔
A data scientist is an analytics expert in gathering, analyzing, and interpreting data to support organizational decision-making. They integrate aspects of various established and specialized professions, such as mathematicians, physicists, statisticians, and computer programmers. It combines scientific ideas with advanced analytic methods like machine learning and predictive modeling.
Roles and Responsibilities🌐
About the Role🙋
The innovative culture at Oracle will help you learn, develop, and advance as a data scientist. This culture thrives on shared success, diversity of thought, and boundaryless chances that can enhance your career in novel and exciting ways.
You will create ground-breaking solutions based on their roadmaps by collaborating closely with customers and a global team of experts using tools like robotics and artificial intelligence. You will develop defense strategies to protect your client that integrates technology and user behavior throughout the blueprint process. Data scientists at Oracle collaborate to connect customers' demands with impending, cutting-edge technology to prepare them for the future.
Some key roles as a data scientist at Oracle are given below but aren't limited to the following-
✍️Put into practice new predictive and prescriptive solutions per corporate demands and specifications.
✍️Convert business problems into precise specifications to create analytical solutions, and find relevant data to back up the solution.
✍️Deliver large-scale initiatives that combine technology and procedures to assist clients in achieving high performance.
✍️In charge of implementing comprehensive big data solutions, such as data collection, archiving, transformation, and analysis.
✍️Manage a region of a project or the entire project to address client analytics issues.
Responsibilities🙋
With great power come greater responsibilities. That's why let's learn some key responsibilities needed to become a data scientist at Oracle.
🎯Provide technical proof points or help the customer see the value of Oracle Cloud Solutions as a data scientist for Oracle.
🎯While promoting solutions, uphold mastery of the Oracle portfolio and in-depth familiarity with market trends, multidisciplinary design patterns, development/deployment techniques, and rival cloud offerings.
🎯Become the customers' trusted advisor.
🎯Create and present business cases, market trends, and competitive differentiators that are in line with the go-to-market plan for your product or service.
🎯Leveraging Oracle Cloud services to produce solutions, demos, and proofs of concepts (POCs) that meet the customer's success criteria
🎯Planning, risk management, stakeholder management, conflict resolution, governance, team management, and ownership of the cloud adoption lifecycle are used to achieve desired goals.
🎯Throughout a prolonged client encounter, be flexible and maintain a sense of urgency and a realistic picture of time (urgent patience).
🎯Design non-routine and elegant business solutions with Oracle products and technology to suit customer demands by combining creativity, independent judgment, and business savvy.
🎯Participate in workshops covering business, applications, information, and technology domains to influence customer strategy, architecture, roadmap, and migration planning.
🎯Keep your expertise at the relevant expert level across the whole data ecosystem, including Big Data, Data Lakes, Advanced Analytics, and Data science.
Well, that's some key roles and responsibilities for a data scientist at Oracle. I hope you all had a thorough understanding of it. Let's see the different perks one enjoys as a data scientist at Oracle.
Salaries and Perks🤑
In India, Oracle's data scientists with between two and nine years of experience earn a yearly income of 16.2 Lakhs. At Oracle India, data scientists earn anywhere from 6.5 to 35 lakhs per year.
Average Annual Salary
₹ 16,15,360
Estimated Take Home Salary
₹ 1,10,285 - ₹ 1,18,339/month
🚨🚨The salary figures mentioned above are subject to change.
Perks as a data scientist at Oracle💷
🔥Free transport
🔥Cafeteria
🔥Team outing
🔥Education assistance
🔥Child care
🔥Paid sick leaves
🔥Work from home
🔥Gymnasium
🔥Soft skill training
🔥Job training
🔥Free food
🔥Health insurance
🔥International relocation
🔥Performance bonus
🔥Paid public holidays
To earn that kind of money, you must have some skills. So let's look at the skill set required for becoming a data scientist at Oracle.
Skills and Experience🙋
Skills💡
Data scientists have been among the world's most highly paid job and in-demand careers for the past five years. Demand began to increase in every area as soon as firms began to see the value of data in their operations. Data science is the foundation for today's analytics, mining or extraction, NLP, ML, AI, and other enterprises.
Some key skills are mentioned below to become a data scientist at Oracle.
Now let's look at the experience required to become a data scientist at Oracle.
Experience🧑💻
🚨Possesses at least three years of relevant expertise in industry or consultancy, including developing and applying advanced analytics/machine learning algorithms.
🚨Know at least one programming language, such as R, Python, PySpark, or SQL.
🚨Become familiar with various Data Science andMachine Learningideas and algorithms, including clustering, regression, classification, forecasting, neural networks, hyperparameters optimization, and NLP.
🚨Recognize that Data Engineering is one of the essential elements of successful analytics delivery and can contribute at any stage of the creation of an analytical model.
🚨Possess knowledge or interest in executing analytical projects on leading Cloud platforms likeGCP, Azure, andAWS.
🚨You should be aware of the ML model lifecycle. A plus would be actual expertise using model lineage tools like MLflow and containerization tools like Docker and Kubernetes.
🚨A plus is having prior experience working in a client-facing or consulting environment.
Now you know all the critical information needed for the job. So what's left? The answer is preparation strategy and all the relevant resources. Don't worry! We have covered that part in the next section of this article.
Now, we will look into the preparation strategy of a data scientist at Oracle.
Preparation Strategy📚
Before becoming a data scientist, you should know why you want to become one. That mindset will provide you with a driving force in critical moments.
After you have decided, we will help you to kickstart your journey.
Motivate yourself to learn data science and create some amazing ideas using it. Practice it consistently and understand new data science concepts one at a time.Before beginning your trip, it will be wise to participate in some workshops or conferences on data science. Set a clear goal for yourself and work toward it.
Now we will provide you with a roadmap, and please follow it to avoid confusion.
Mathematics🚀
Math aptitude is crucial since it aids in our knowledge of numerous machine learning and data science methods. Be equipped with fundamentals like linear algebra, vector calculus, matrix, geometry, etc. You can refer to the blog here for more information.
Probability🚀
It is considered one of the prerequisites for mastering machine learning. Refer to the blog here for more information.
Statistics🚀
Understanding the concepts of statistics is important for data analysis. Refer here for more knowledge.
Language🚀
Programming principles like data structures and algorithms must be well-understood. Python, R, Java, and Scala are the programming languages used. In some situations where efficiency is crucial, C++ can also be helpful. A good grasp of libraries like numpy, pandas, and tensorflow is very important to excel in data science.
Databases🚀
Having command over the databases is crucial too. You can learn about databases like SQL and MySQL.
Machine Learning🚀
Every year, new developments are made in machine learning (ML), one of the most important components of data science and the most popular research topic. At the very least, one must be familiar with the fundamental algorithms of supervised and unsupervised learning.PythonandRhave various libraries that can be used to implement these techniques. Refer to the blog here for a better understanding.
Deep Learning🚀
Well, you can't be confident in your skills if you aren't confident about deep learning. Having a thorough understanding of all the networks is important. Refer to the blog here for more understanding.
Feature Engineering🚀
It is the most important part of any data science project. You can't achieve anything without learning about feature engineering.
Others🚀
Having domain knowledge about the project, deployment skills, and skilled in data visualization tools are some of the factors that decide your expertise as a data scientist. So, learning about them is crucial too.
Lastly, keep practicing; you can't be an expert if you can't practice. Remember that these are the building blocks of your career as a data scientist.
Career Map📈
The career of the data scientist starts with 🏄🏻♀️.
Junior Data Scientist🏄🏻
Junior data scientist is the starting position. It is regarded as the entry-level where a candidate joins after completing the selection process.
Data Scientist🏄🏻♂️
Data scientist level is achieved typically after getting 1 to 3 years of experience as a junior data scientist. The data scientist should be an expert in data analysis, analytical skills, research, python, R, SQL, machine learning, communication skills, and teamwork.
Senior Data Scientist🏂🏻
Senior data scientist level is usually achieved after 3 to 5 years as a data scientist. A senior data scientist understands the overall business picture. They have expertise in the best data analytics technologies, maintain budgets and prevent fraud.
Principal Data Scientist👨✈️
Experience of ten or more years is required to become a principal data scientist. This position is the most experienced among all data science team members.
Note⚠️ - The career path of a data scientist may vary a little from company to company.
So buckle up, fellow data scientist! You are the future.
Cool! Check the video given below for some more help.
Frequently Asked Questions
Who is a data scientist?
A data scientist is an analytics expert in gathering, analyzing, and interpreting data to support organizational decision-making. They integrate aspects of various established and specialized professions, such as mathematicians, physicists, statisticians, and computer programmers.
How much does a data scientist at Oracle make?
Oracle Data Scientist salary in India ranges between ₹6.5 Lakhs to ₹37.0 Lakhs with an average annual salary of ₹16.2 Lakhs.
Which Oracle certification is best for data science?
Oracle Certified Expert (OCE) certification is best for data science.
Does being a data scientist require SQL?
SQL is required for handling structured data by a data scientist. Given that relational databases are used to store structured data. Therefore, a data scientist must be well-versed in SQL commands to query these databases.
What is Oracle machine learning?
Using SQL, R, Python, REST, AutoML, and no-code interfaces, Machine Learning in Oracle Database provides data exploration, preparation, and large-scale machine learning modeling. More than 30 high-performance in-database techniques are used to create models that may be used in apps immediately.
Conclusion
This blog covered the job role of a data scientist at Oracle. We went through responsibilities with salary and perks, skills and experience required, a Career path, and a roadmap to become a Data Scientist at Oracle. To learn more, check out our articles on
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