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Introduction👀
Do you want a job as a data scientist at Accenture? Don't worry; we will provide a clear roadmap that many ninjas follow to bag offers from top companies like Accenture.
Don't be afraid because
This article will discuss becoming a data scientist at Accenture.
But before jumping into the preparation strategy for becoming a Data Scienistat Accenture, let's learn some interesting facts about Accenture. So, let’s get started.
About Accenture👀
Accenture is a global professional services company with industry-leading digital, cloud, and security expertise. Offering strategy and consulting Technology and Operations Services, and Accenture Song, powered by the greatest global network of Advanced Technology and Intelligent Operations centers. Accenture combines unrivaled knowledge and specialized talents across more than 40 sectors.
Every day, 699,000 employees serve customers in more than 120 countries by delivering on the potential of technology and human imagination. Accenture embraces the power of change to create value and shared success for its clients, employees, shareholders, partners, and communities.
Well, it's a sure thing learning about the company must have gotten you enthusiastic about learning more about the data scientist role at Accenture. Without further ado, let's dive directly into that.
Roles and Responsibilities🌐
About the Role🙋
The innovative culture at Accenture 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 Accenture collaborate to connect customers' demands with impending, cutting-edge technology to get them ready for the future.
Some key roles as a data scientist at Accenture 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.
✍️Operate with little oversight.
Responsibilities📜
With great power comes greater responsibilities. That's why let's learn some key responsibilities that need to be fulfilled to become a data scientist at Accenture.
🎯To provide value to clients, and develop insights using predictive statistical modeling.
🎯Find correlations between and among variables using sophisticated statistical and mathematical knowledge, tools, and strategies.
🎯Create an intellectual property for specific analytic procedures, mathematical modeling, or optimization methods and how they are applied to commercial problems.
🎯Convert business problems into precise specifications to create analytical solutions, then find the relevant data to back up the solution.
🎯Using strategy, industry, and analytical skills, work with Accenture and outside consulting teams to address business issues and influence change.
🎯Cloud and machine learning technologies enable at-scale Data science capabilities and enhance client enterprises.
🎯Accenture is looking for specialists with a range of experience levels. Your level of accountability, responsibility, and autonomy will depend on your experience and seniority.
Well, that's some of the key roles one has to play as a data scientist at Accenture. I hope you all had a thorough understanding of it. Let's see the different perks one enjoys as a data scientist at Accenture.
Salaries and Perks🤑
In India, Accenture's Data Scientists with between two and nine years of experience earn a yearly income of 12.3 Lakhs. At Accenture India, data scientists earn anywhere from 5.4 to 24.0 lakhs per year. Salary estimates for IT services & consulting Companies are based on 742 salaries submitted by various Accenture India employees.
✴️Note: The average salary of a data scientist at Accenture is ₹12.3 Lakhs per year which is 29% more than the average salary of a Data Scientist in India who receives a salary of ₹9.5 Lakhs per year.
👉A typical Data Scientist’s salary at Accenture is(Annually)
₹5,40,000 - ₹24,000,000
👉Average Annual Salary
₹12,30,000
Average Annual Salary
₹ 12,30,000
Estimated Take Home Salary
₹ 87,082 - ₹ 89,365/month
🚨🚨The salary figures mentioned above are subject to change.
Perks as a data scientist at Accenture💷
🔥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
Well, to earn that kind of money, you must have some skills needed inside you. So let's look at the skill set required for becoming a data scientist at Accenture.
Skills and Experience🙋
Skills💡
Data scientists have been one of the world's most highly prized 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 Accenture.
Now let's look at the experience required to become a data scientist at Accenture.
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 are 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 Accenture.
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. You can refer to the blog herefor a guide.
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 begin to understand new concepts in data science 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 I 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. Python and R have 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. I would suggest you read as many articles' documentation as possible. Reading expands your horizon even far.
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 types of 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.
Resources for Preparation🚨
Alright! Here are the resources to help you prepare for your recruitment exam and interview.
Well, that's the end of the article. I have added some FAQs to clear up any doubts.
Frequently Asked Questions
What are the working hours at Accenture?
The work hours at Accenture were described as flexible by 67% of the workforce. Contrarily, 33% said it was strict, with the majority citing the hours of 9 am to 6 pm and 10 am to 7:30 pm.
What does Accenture do?
Business strategy, technology strategy, operations strategy, as well as technology, business, and management consulting services, are all offered by Accenture Strategy and Consulting.
How much does a data scientist at Accenture make?
Accenture Data Scientist salary in India ranges between ₹5.4 Lakhs to ₹23.0 Lakhs with an average annual salary of ₹12.3 Lakhs.
Does Accenture use machine learning?
We harness the strength of fields like strategy, deeply functional and industry knowledge, and competence in deep machine learning, data science, and data architecture and engineering.
Who is a data scientist?
Data scientists are a new breed of analytical data experts with the technical know-how to address complicated issues and the curiosity to investigate what issues are at stake. They have elements of mathematicians, computer scientists, and trend-spotters.
Conclusion
This blog covered the Job role of a data scientist at Accenture. We went through responsibilities as Data Scientists with salary and perks, skills and experience required, a Career path as Data Scientists at Accenture, and a roadmap to becoming a Data Scientist at Amdocs. To learn more, check out our articles on
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