This content originally appeared on HackerNoon and was authored by Aditi Godbole
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:::tip HackerNoon editorial team has launched this interview series with women in tech to celebrate their achievements and share their struggles. We need more women in technology, and by sharing stories, we can encourage many girls to follow their dreams. Share your story today!
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Tell us about yourself!
I am Aditi Godbole—a Senior Data Scientist at SAP with over 11 years of experience in AI and machine learning. My journey began over a decade and a half ago when Data Scientist roles were not considered the sexiest jobs of the 21st century. It all started with a fascination for image processing, neural networks, and digital signal processing a combination that led me through roles in diverse industries like Semiconductors, Power, and Automotive. At SAP, I am shaping AI strategies for integrated spend management. My expertise covers supervised learning, NLP, and Generative AI. I am particularly proud of developing a patented design that fuels one of the leading products in SAP's product portfolio. Beyond technical work, I am dedicated to mentoring and knowledge-sharing in the Data Science community. I regularly speak at conferences and contribute to open-source projects. What drives me is the potential of AI and ML to transform businesses. I am constantly excited to uncover insights from data and create impactful solutions. I am always eager to explore how we can leverage data to shape a smarter, more efficient future. \n
Why did you choose this field in the first place?
During my studies, I found myself captivated by subjects like neural networks, probability theory, and pattern recognition. These subjects weren't just academically interesting—they offered powerful tools to solve complex, real-world problems. I built a project on American Sign Language detection using image processing and neural networks, which sparked my curiosity about the potential of these technologies. What really solidified my choice was seeing how these techniques could be applied across various domains. Starting with work on camera imaging pipelines and then moving to projects involving diverse sensor data to predict the remaining useful life of gas turbines. This experience showed me how data could be harnessed to create meaningful solutions. As I progressed in my career and had the opportunity to work with diverse datasets, I realized the immense potential of data in driving business decisions and creating value. It's a constantly evolving field, always presenting new challenges and opportunities to make a tangible impact. The ability to extract meaningful insights from data and use them to drive decision-making felt like unlocking a superpower and that excitement continues to drive me today.
What tech are you most excited/passionate about right now and why?
I am most excited about the advancements in Generative AI and its applications in Natural Language Processing (NLP). The ability of Generative AI to create content, whether it's text, images, or even code, represents a significant leap forward in what AI can achieve. It's not just about automation anymore; it's about augmenting human creativity and expanding the possibilities of what can be done with data. In the context of NLP, Generative AI is opening up new avenues for building more intuitive and conversational interfaces, improving language translation, and even generating synthetic data for training more robust machine learning models. I am particularly passionate about how these technologies can be leveraged to create more personalized and intelligent systems that can understand and respond to human needs more effectively. The potential of Generative AI to revolutionize industries, from content creation to software development, is immense, and I am excited to be working in a space where I can contribute to these cutting-edge developments.
What tech are you most worried about right now and why?
While I am excited about AI's potential, I am also deeply concerned about how malicious actors could potentially exploit rapid advancements in technologies like deepfakes and autonomous systems to cause harm. Deepfake technology, for instance, has advanced to a point where it's becoming increasingly difficult to distinguish between real and fabricated content. This raises serious concerns about misinformation, privacy violations, and the potential for malicious use in areas like politics, media, and personal privacy. Autonomous systems, especially in critical sectors like transportation and the military, also worry me. The decision-making processes of these systems are often opaque, and unintended consequences could arise if they malfunction or are used without proper oversight. The ethical implications of allowing machines to make decisions that could affect human lives are profound and demand careful consideration. The challenge lies in balancing innovation with responsibility. As we continue to push the boundaries of what technology can do, it's crucial to develop robust frameworks for ethical AI development, ensuring that these powerful tools are used for the greater good without compromising safety and trust.
What are your hobbies and interests outside of tech?
While I am passionate about AI, I believe in maintaining a healthy work-life balance. As a parent of a toddler, family time is my top priority. I cherish moments spent with my little one, whether it's playtime, storytime, or just watching them explore the world with wonder. My curiosity about AI doesn't completely switch off outside work. I often find myself keeping up with the latest developments and occasionally tinkering with new AI tools. It's a genuine interest that keeps me inspired. For relaxation, I love spending time in the kitchen. There's something therapeutic about trying new recipes – it's a bit like working with data, following a process but with room for creativity! When I carve out quiet time, I enjoy diving into light-hearted novels, particularly romances and comedies. They're my go-to for relaxation after a day of complex algorithms. These diverse interests help me maintain perspective and bring fresh energy to my professional life.
Let's talk about breaking the glass ceiling. What were the biggest challenges you faced as a woman in tech, and how did you deal with them?
Breaking the glass ceiling in tech has been a journey filled with challenges, many of which stem from being a woman in a traditionally male-dominated field. One of the biggest challenges I faced was dealing with the subtle biases and assumptions that women often encounter in the workplace. For instance, I've been in situations where my technical expertise was questioned or where I had to work harder to prove my capabilities compared to my male counterparts. Navigating these biases required a mix of resilience, confidence, and continuous learning. I made it a point to consistently deliver high-quality work, letting my achievements speak for themselves. This helped to slowly shift perceptions and establish my credibility in the field. I also sought out mentors and allies who supported my growth, offered guidance, and helped amplify my voice in critical discussions. To deal with these challenges, I focused on building a strong network of supportive peers, mentors, and sponsors who could provide advice and advocacy when needed. I also became more involved in initiatives that promote diversity and inclusion in tech, which allowed me to contribute to creating a more equitable environment for others as well. Ultimately, these experiences have shaped me into a stronger leader and advocate for women in tech. They've driven me to mentor other women in the field, helping them navigate similar challenges and encouraging them to break their glass ceilings.
Any questionable misogynistic story/situation you faced/handled, and you want to share with the HackerNoon Fam?
Early in my career, I experienced a situation that underscored the subtle ways misogyny can manifest in the workplace. During a team meeting, I presented a solution to a technical problem, only to have my idea dismissed. Minutes later, a male colleague suggested the exact same approach, and it was met with enthusiasm. Instead of confronting the situation immediately, I chose to let my work speak for itself. Over time, I consistently delivered strong results, and eventually, the same colleagues who dismissed me began to recognize my expertise. However, this experience made me more aware of the biases women face in tech. I've since made it a point to advocate for others whose voices might be overlooked. By fostering a supportive and inclusive environment, we can challenge these biases and ensure that everyone's contributions are valued, regardless of gender. \n
What was the biggest setback/failure that you faced, and how did you manage it?
I faced a major setback early in my career. I was working on predicting the remaining useful life of a gas turbine. We trained a model and tested it on new data. The model performed very poorly. This surprised us. I led the team in analyzing the problem. After a thorough review, we found the issue. The test data was low quality. This taught me the importance of "garbage in, garbage out". Since then, I've become a strong advocate for high-quality data. This experience showed me how crucial good data is for accurate predictions. It changed how I approach data science projects. Now, I always prioritize data quality by performing data analysis before I start model training or inference \n
What's your biggest achievement that you're really proud of?
One of my biggest achievements that I am particularly proud of is the development of a Configurable Entity Matching system, which I designed and patented. This system is pivotal for SAP's AI-driven efficiencies and future revenue growth. It transforms complex data into intuitive analytics dashboards, offering a comprehensive view of spend metrics, supplier performance, and product segment analysis. The system not only addresses intricate data challenges but also supports scalable business operations. Its successful implementation has had a significant impact on enhancing operational efficiency and decision-making processes within SAP. This achievement is a testament to my commitment to advancing AI and machine learning technologies and demonstrates the tangible benefits that innovative solutions can bring to an organization. \n
In your opinion, why do we see this huge gender gap in the tech industry, and how can we reduce it?
The gender gap in tech stems from stereotypes, lack of representation, and systemic biases. Early education plays a key role; if girls aren't exposed to STEM or encouraged to pursue it, they may shy away from tech careers. To address this gap, we need to promote STEM education for girls from a young age, offering mentorship and hands-on experiences to build confidence and interest. Creating inclusive work environments is crucial; tech companies should implement policies to combat unconscious biases and ensure fair recruitment and growth opportunities. Increasing the visibility of women in tech through leadership roles and media representation can provide inspiring role models. Additionally, establishing strong support networks and communities for women in tech can offer essential resources and professional development. By tackling these areas, we can bridge the gender gap and foster a more equitable and diverse tech industry. \n
Who is your tech idol? Why?
In the field of AI and Machine Learning, I greatly admire Andrew Ng. His contributions to the field, particularly in deep learning, have been groundbreaking. But what I find most inspiring about him is his dedication to education and democratizing AI knowledge. Through his online courses and initiatives like deeplearning.ai, he's made complex AI concepts accessible to millions worldwide. His ability to bridge the gap between cutting-edge research and practical applications in industry is something I aspire to in my own career. Ng's vision of AI as a transformative force for good, and his emphasis on ethical AI development, align closely with my own values and professional goals.
Do you have any advice for aspiring girls who want to join the field?
For aspiring girls interested in tech, start early by engaging with coding or data science activities to build a strong foundation. Seek out mentors who can offer guidance, support, and valuable industry insights. Stay curious and committed to continuous learning, as the tech field is constantly evolving. Building a network by joining tech communities, attending meetups, and participating in hackathons can provide both opportunities and support. Finally, be confident in your skills and achievements, advocate for your career development, and don’t hesitate to seek out opportunities. Persistence and passion are key, and your unique perspective in tech is invaluable.
This content originally appeared on HackerNoon and was authored by Aditi Godbole
Aditi Godbole | Sciencx (2024-08-27T16:12:57+00:00) Breaking Barriers in AI: Insights from Aditi Godbole, a Senior Data Scientist at SAP. Retrieved from https://www.scien.cx/2024/08/27/breaking-barriers-in-ai-insights-from-aditi-godbole-a-senior-data-scientist-at-sap/
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