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It can be hard to have a 1-1 conversation with senior data professionals, especially when you’re just starting. This interview-style article aims to get a better understanding of the senior-level data professional journey and advice, to provide you with the resources to self-reflect on your journey in the data world.

Let’s start…

How did you Become a Senior Software Engineer at Microsoft?

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 an undergraduate degree in Computer Science and Engineering at NIT Warangal, where I graduated in 2015. I then joined Microsoft through a campus placement, in which I later joined the Bing Maps team within the Search and AI organisation. 

In my time with 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 enhance label density detection on 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 coupled with product ads, playing a significant role in bolstering Bing's revenue. I love innovating and solving everyday problems. I have won numerous hackathons throughout my career, the last one being 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 filled with unknowns and a determination to solve complex problems.

How can Data Professionals Transition into AI?

I think the move from data science or analytics to AI is often smoother than people realise. Both fields demand a strong foundation in math and programming. But, if you're a data professional wanting to pivot, you will need to drill down on machine learning algorithms and neural networks.

What Educational Background is Necessary?

One of the first questions professionals usually ask is the educational prerequisites for getting into AI. Do you need a Ph.D., or will a bachelor’s or master's degree suffice? 

The answer varies depending on the role and the company. While a Ph.D. can be beneficial, especially for research positions, it's not a strict requirement. A bachelor's or master's degree in computer science, mathematics, or a related field can suffice. 

What’s crucial is a deep understanding of the principles of AI and machine learning, which can be acquired through specialized courses and self-study.

Are Certifications Useful?

Certifications can help demonstrate your interest and foundational knowledge in AI, especially when transitioning from a different field. But they should complement your education and experience, not replace them. It's important to note that certifications are not a golden ticket. 

They serve best when used to supplement real-world experience and a solid foundational education. Employers typically look for hands-on experience and problem-solving capabilities, which can sometimes be gained outside of certification programs.

Are there Recommended Pathways or Courses?

Skipping the basics is a bad idea. Start with fundamental courses in linear algebra, calculus, and statistics. 

From there, I recommend diving into machine learning, possibly through online courses like Coursera’s Machine Learning Course by Andrew Ng. EdX and Udacity also offer programs like the MicroMasters in Artificial Intelligence and Nanodegrees in AI, respectively. 

Then, explore specialized courses or projects that align with your interests, be it natural language processing, computer vision, or reinforcement learning.

What are the Must-Learn Technologies and Tools?

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 for designing, training and validating models with efficiency and scalability. Jupyter Notebooks are also crucial for prototyping and sharing models with peers. 

Beyond the language and libraries, knowing your way around cloud-based AI services such as Azure AI or AWS SageMaker can set you apart from the pack.

How can Someone Gain Practical Experience?

Theoretical knowledge is important, but you'll also need hands-on experience. 

One effective way is by engaging in personal projects. Tailor these projects to solve problems you’re passionate about or that address gaps in current technology—this will make the learning process more enjoyable and the outcome 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 participating in competitions, like those on Kaggle, which challenge you to apply your skills to novel problems and learn from the global community. 

Internships are invaluable, offering mentorship and hands-on experience in industrial settings. Even if unpaid, the practical knowledge gained can be a significant stepping stone. Practical experience isn’t just about coding—it’s also about understanding how AI can be deployed effectively to solve real-world issues. 

Therefore, through project work, collaborations, and competitions, you can build a portfolio that showcases your ability to deliver AI solutions with tangible impact.

What's the Role of Networking?

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 shy away from asking questions. Relationships can open doors that may otherwise remain closed. Real-world problems offer the best learning experiences.

What Helped You? What Would You Have Done Differently?

What propelled me forward was a blend of curiosity and the drive to tackle the unknown, which guided my project leadership at Microsoft. 

If I could revisit the past, I'd emphasize networking even more. Building relationships within the industry can open doors to collaborative opportunities and insights that are invaluable in a field as dynamic as AI. 

I'd also allocate more time to personal projects to innovate freely without constraints, allowing a fuller exploration of AI's possibilities and perhaps, even more, groundbreaking contributions to the field.

Wrapping it Up

Manas Joshi 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 have been able to learn about Manas’ experience, take on board his advice, and have a better understanding of the skills necessary for data professionals eager to break into the ever-evolving field of AI.
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.