Interview with Director of Data Science, Jyotika Singh

Tell us about the business you represent, what is their vision & goals?

Placemakr embodies an innovative tech-driven hospitality platform, orchestrating captivating hospitality living experiences and captivating pop-up properties across the vast expanse of the United States. Our fundamental approach centres around harnessing the power of AI and Machine Learning to drive data-centric decision-making processes. Data empowers us not only to optimize pricing strategies and formulate shrewd business tactics in the present, but also to enhance customer retention, streamline cost efficiency, foster perceptive budgeting, and master the art of inventory management.

Can you share a little bit about yourself and how you got into the field of artificial intelligence?

During my formative years, the concept of AI captivated my imagination, albeit within the confines of envisioning robots executing human tasks. It was only later that I grasped the profound breadth of AI, extending far beyond the realm of robotics.

While pursuing my Master's degree in Electrical and Computer Engineering at UCLA, specializing in speech processing, I witnessed the nascent integration of machine learning into the analysis of language data. This pivotal moment marked the beginning of my unwavering dedication to researching and embarking on numerous machine-learning projects. Upon graduating, I secured a coveted position as a junior data scientist, initiating a transformative journey of over seven years, during which I immersed myself in the exploration and implementation of AI.

Over the course of this journey, I have been exceptionally fortunate to encounter remarkable opportunities. These include authoring multiple patents, laying the foundation of a company's data science assets from inception and cultivating their growth into multimillion-dollar ventures. I have played a pivotal role in facilitating a company's acquisition, authored a book on AI, authored open-source Python libraries, and consistently developed strategies to harness the power of data and machine learning, thereby augmenting business value and driving success.

Through these enriching experiences, I have deepened my understanding of the symbiotic relationship between data-driven insights and business prosperity, and I remain committed to advancing the frontiers of AI in service of empowering enterprises and driving meaningful impact.

What does your day-to-day responsibilities look like at your organization?

In my day-to-day work, I focus on leading data science development at the company and transforming complex business challenges into actionable strategies, research, and implementation. I am responsible for maximizing revenue for high-income properties across the United States, and I achieve this by building yield optimization tools that directly impact annual revenue of over 14 million USD.

One crucial aspect of my role involves leveraging Large Language Models (LLMs) and classic machine learning to perform customer feedback analytics. By harnessing the power of these advanced models, we gain valuable insights from customer feedback, which helps inform decision-making and drive improvements. Additionally, I apply my expertise in statistics, machine learning, deep learning, natural language processing, and time-series forecasting to develop data science strategies. These strategies enable me to enhance pricing strategies, optimize inventory management, and improve overall property performance.

Overall, my day-to-day activities involve tackling complex business challenges, developing and implementing advanced data science techniques, and providing actionable insights to drive revenue maximization, customer satisfaction, and overall business success.

What are some misconceptions that you believe the average person has about AI?

To individuals less familiar with the intricacies of the technology field, AI often remains shrouded in mystery—an enigmatic concept evoking images of robots and unsettling concerns of machines surpassing human capabilities and gaining sentience. However, the reality of AI is far simpler than what many perceive. At its core, AI is a product of human creation, designed to learn and adapt based on human-defined objectives. It cannot generate knowledge that is beyond human comprehension.

Consider the remarkable capabilities demonstrated by ChatGPT and its widespread popularity. Have you ever wondered how it possesses such extensive knowledge? Behind the scenes, the technology is developed through a process of training a machine on vast amounts of data harvested from the internet. Essentially, it has been taught based on the information we, as humans, have generated. Due to their innate capacity to process and retain immense quantities of data far surpassing human capabilities, machines offer distinct advantages across applications. While significant milestones have undeniably been achieved within the realm of AI, there remains much more to accomplish. AI possesses immense power and potential to enhance human capabilities and propel progress across diverse domains when wielded adeptly.

What advice would you give to someone wishing to start a career in artificial intelligence?

In my capacity as a volunteer mentor at Data Science Nigeria and Women Impact Tech, I frequently engage with numerous individuals who harbour aspirations of building a career in AI. In light of my experiences, I offer the following advice: establishing a strong foundation is paramount. If you have not yet embarked on a formal educational path, such as pursuing a degree, enrolling in a course, or participating in a boot camp, I highly recommend initiating your journey. You can start by exploring the diverse range of courses available on platforms such as Coursera and similar reputable resources. This step will enable you to develop a comprehensive understanding of the subject matter.

Once you have acquired a solid theoretical background, the subsequent crucial step involves gaining practical experience. Embrace project-based endeavours that not only embellish your resume with tangible hands-on experience but also facilitate the application of your acquired knowledge. An abundance of openly accessible datasets awaits exploration, providing limitless opportunities to augment your skills through firsthand engagement. Embracing these projects will not only enhance your practical aptitude but also deepen your comprehension of real-world implementations.

The realm of AI encompasses an expansive array of professions, including roles such as Data Scientists, Data Analysts, Machine Learning Engineers, and various others. Acquiring a fundamental understanding of the distinct characteristics and responsibilities associated with these job roles becomes pivotal in charting your desired trajectory within this field.

Moreover, I can vividly recall the initial stages of my personal journey, wherein I often grappled with a sense of pressure to possess knowledge that had not yet been acquired. It is imperative to recognize that, as one ventures into a new field, it is not only acceptable but also anticipated to encounter areas in which one's knowledge is incomplete. Even experts within the field are continually engaged in a process of learning and growth. Embrace this natural progression as an integral part of your career trajectory, understanding that ongoing learning is an intrinsic component at every stage.

Would you like to share any artificial intelligence forecasts or predictions of your own with our readers?

The pace of AI adoption within businesses has demonstrated remarkable growth, catalyzed by the emergence of groundbreaking technological innovations such as ChatGPT. These advancements not only serve to popularize AI but also raise awareness about its transformative potential. However, what lies ahead is a paradigm shift, as I foresee a substantial surge in the adoption of AI and machine learning by businesses—a surge that will surpass the current level of implementation. Countless enterprises today possess vast amounts of untapped data, harbouring immense potential waiting to be harnessed.

In the realm of research, the development of large-scale models, particularly large language models (LLMs), stands as a prevailing area of focus. The ongoing pursuit involves refining these models to optimize resource utilization by creating smaller, more efficient versions. This pursuit will undoubtedly witness a multitude of pioneering advancements in the coming years.

We are already witnessing early indicators of this transformative wave, as evidenced by the escalating integration of AI into our everyday lives. A noteworthy example is the recent Vision Pro launch announcement by Apple, showcasing machine learning-driven developments that possess the capability to seamlessly integrate into our real-world experiences.

These developments collectively highlight the trajectory of AI's impact, with its integration poised to permeate various aspects of our lives, shaping industries, enhancing productivity, and revolutionizing how we interact with technology on a daily basis.

What is your experience of using AI-backed data tools?

In my previous position, we utilized the machine learning capabilities of Google Cloud Product (GCP) to rapidly prototype models by simply using a query. This streamlined approach provided great convenience, as it enabled individuals with limited machine learning expertise to build, test, and utilize models. We could even call these ML models directly from Google Sheets, expanding the accessibility and ease of use.

At Placemakr, we actively harness the power of AWS Sagemaker for prototyping, deploying, testing, and monitoring machine learning models. The diverse range of services offered by AWS Sagemaker significantly reduces the setup time, allowing our data scientists to focus on conducting experiments and advancing their research. One aspect I particularly found valuable is the use of Sagemaker endpoints, which enable us to deploy serverless machine learning models for applications to seamlessly access. This approach ensures that the model and its associated processes remain completely independent from the applications that leverage them. Furthermore, we are exploring Microsoft's Power BI, a popular tool primarily renowned for data visualization and business intelligence. However, Power BI also offers functionalities for integrating and harnessing machine learning techniques and models within data analysis workflows. This integration allows users to enhance data analysis capabilities with the power of machine learning.

Are there any books, blogs, or other resources that you highly recommend on the subject of AI?

In the current era, aspiring practitioners of machine learning have access to a plethora of resources to embark on their journey. I am delighted to announce my attempt to contribute to the ever-growing pool of AI resources with the publication of my book titled "Natural Language Processing in the Real World," published by CRC Press, focussing on AI using text data.

Having traversed my own path in machine learning and natural language processing (NLP), I felt compelled to share my insights and knowledge with others. During the transition from being a student to entering the workforce, I encountered numerous challenges with limited guidance to navigate this transformation. With beginners in mind, it is intriguing to consider that certain aspects taught during our educational pursuits, such as implementing the k-nearest neighbors algorithm from scratch, may not hold the same practical significance in real-world scenarios. In practice, leveraging existing tools for machine learning implementations takes precedence, with custom solutions rarely being necessary. Furthermore, did you know that opting to switch from logistic regression to convolutional neural networks (CNNs) may yield a marginal 2% improvement in accuracy, making it a questionable endeavour? Indeed, simpler and more compact models often outperform their complex counterparts. Real-world AI implementations prioritize practicality, and it is precisely for this reason that I endeavoured to write this book.

"Natural Language Processing in the Real World" serves as a comprehensive guide, encapsulating my accumulated learnings. The book presents a collection of case studies that offer insights into the diverse applications of NLP across more than 15 industry verticals, providing a comprehensive understanding of how they leverage this technology. Within its pages, readers will find a wealth of practical tips and insights, offering not only a plethora of NLP applications across various industry verticals but also Python-based implementation code for popular use cases. Delving into the book, readers will gain a deep understanding of why certain projects hold significance for companies and how to construct them utilizing available tools and Python.

Additionally, among the wealth of resources in AI and Machine Learning, one particularly notable figure is Andrew Ng, whose courses have stood the test of time and proven to be exceptionally user-friendly. His courses, available on platforms like Coursera, provide an excellent starting point for those seeking to acquire knowledge in the field.

For aspiring data scientists eager to gain hands-on experience in constructing machine learning models, the UCI repository stands out as a valuable resource. It houses numerous publicly available datasets that can be utilized for exploration and experimentation, enabling practitioners to apply their skills and refine their understanding.

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