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It can be difficult to have a one-on-one conversation with high-level data professionals, especially when you’re just starting out. This interview-style article aims to gain a better understanding of the senior data professional’s journey and advice, to give you the resources to reflect on your journey in the world of data.
Let us begin…
My journey into the world of ai and software engineering began in my childhood with a keen interest in programming. This passion led me to pursue a degree in Computer Science and Engineering at NIT Warangalwhere I graduated in 2015. Then I joined microsoft through a campus placement, where I then joined the Bing Maps team within the Search and ai organization.
In my time at Bing Maps, I contributed to several projects aimed at improving the service. My most notable contribution was leading the development of a new machine learning algorithm to improve label density detection in maps. I wrote a research paper on the new technique that received several awards and was published in the Microsoft Journal of Applied Research.
After maps, I became a founding member of the Bing Shopping vertical. There, I led the launch of multiple features along with product announcements, playing a major role in bolstering Bing’s revenue. I love innovating and solving everyday problems. I’ve won numerous hackathons throughout my career, the last one was where I created an ai chatbot designed to streamline online grocery shopping. Currently, I’m back at Bing Maps, working on innovative ways to refine and expand our mapping services.
The key to my career growth has been a relentless drive to lead projects full of unknowns and a determination to solve complex problems.
I think the move from data science or analytics to ai is often easier than people think. Both fields require a solid foundation in mathematics and programming. But, if you are a data professional and want to pivot, you will need to delve deeper into machine learning algorithms and neural networks.
One of the first questions professionals often ask is the educational prerequisites for entering ai. Do you need a PhD or will a bachelor’s or master’s degree suffice?
The answer varies depending on the position and company. While a Ph.D. can be beneficial, especially for research positions, it is not a strict requirement. A bachelor’s or master’s degree in computer science, mathematics, or a related field may be sufficient.
What is crucial is a deep understanding of the principles of ai and machine learning, which can be acquired through specialized courses and self-study.
Certifications can help demonstrate your interest and basic knowledge in ai, especially when transitioning from a different field. But they should complement your education and experience, not replace them. It is important to note that certifications are not a golden ticket.
They serve best when used to complement real-world experience and a solid basic education. Employers often look for practical experience and problem-solving skills, which can sometimes be obtained outside of certification programs.
Skipping the basics is a bad idea. Start with foundational courses in linear algebra, calculus, and statistics.
From there, I recommend diving into machine learning, possibly through online courses like Coursera Machine Learning Course by Andrew Ng. EdX and Udacity We also offer programs such as MicroMasters in artificial intelligence and Nanodegrees in ai, respectively.
Then, explore specialized courses or projects that align with your interests, whether it’s natural language processing, computer vision, or reinforcement learning.
While Python remains the go-to language in both fields, for ai you’ll also need to get your hands dirty with specialized libraries like TensorFlow and PyTorch. They provide the building blocks to design, train, and validate models with efficiency and scalability. Jupyter Notebooks They are also crucial for prototyping and sharing models with peers.
Beyond the language and libraries, knowing cloud-based ai services, like Azure ai or AWS SageMaker, can set you apart from the rest.
Theoretical knowledge is important, but you will also need practical experience.
An effective way is to participate in personal projects. Tailor these projects to solve problems you are passionate about or that address gaps in current technology; This will make the learning process more enjoyable and the result more impactful.
Additionally, contributing to open source projects can not only hone your skills but also get you noticed in the community. Another avenue is to participate in contests, such as those on Kaggle, that challenge you to apply your skills to novel problems and learn from the global community.
Internships are invaluable and offer mentorship and hands-on experience in industrial settings. Even if they are not paid, the practical knowledge gained can be an important step forward. Hands-on experience is not just about coding, but also about understanding how ai can be implemented effectively to solve real-world problems.
Therefore, through project work, collaborations and competitions, you can build a portfolio that showcases your ability to deliver ai solutions with tangible impact.
Networking is vital. Attend ai meetups, webinars, and conferences. Follow thought leaders in the field on social media. Engage in discussions, seek mentorship, and don’t avoid asking questions. Relationships can open doors that would otherwise remain closed. Real-world problems offer the best learning experiences.
What propelled me forward was a mix of curiosity and the drive to confront the unknown, which guided my project leadership at Microsoft.
If I could revisit the past, I would emphasize networking even more. Building relationships within the industry can open doors to opportunities for collaboration and insights that are invaluable in a field as dynamic as ai.
I would also allocate more time to personal projects to innovate freely and without restrictions, allowing for a fuller exploration of the possibilities of ai and perhaps even more innovative contributions to the field.
Joshi Manas He is a Senior Software Engineer at Microsoft and has led several projects across the Microsoft Bing ecosystem with expertise in ai, NLP, and machine learning. In this article, we hope you were able to learn about Manas’ experience, take his advice, and better understand the skills needed for data professionals eager to enter the ever-evolving field of ai.
nisha arya is a data scientist and freelance technical writer. She is particularly interested in providing professional data science advice or tutorials and theory-based data science insights. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
