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WiDSTORY | September 5, 2023

From Mechanical Engineer to Data Science Pioneer: Faraz Rahman’s Journey with WiDS and Beyond

Meet Faraz Rahman, a versatile data science professional with over 10 years of experience in diverse engineering fields, including Manufacturing, Renewable Energy, and Defense. Equipped with a holistic understanding of the Machine Learning product development lifecycle, she has excelled as a data scientist, ML engineer, and product manager. Her passion for data science was ignited while analyzing wind energy generation data, driving her to master Python, R, and SQL. Currently pursuing graduate studies at Carnegie Mellon University, she aims to lead impactful AI and ML projects. Faraz credits WiDS for boosting her confidence and inspiring her to submit a presentation proposal that encouraged first-time speakers to participate at a regional conference. Her ultimate goal is to leverage data science and AI to address pressing global challenges and empower more women to explore the possibilities of this field through inclusive communities like WiDS.

Tell us about your background.

I am a mechanical engineer turned data science and analytics professional with over 10 years of experience in applying analytics in core engineering fields such as Manufacturing, Strategic Planning, Defense, Renewable Energy, Education, Precision Agriculture, and Remote Sensing Technology. Throughout my career, I have gained valuable experience in identifying business pain points and providing descriptive, predictive, and prescriptive analytics solutions to customers from around the world. I have experience in building and deploying machine learning products in fast-paced early-stage startups and that has allowed me to develop a diverse skill set and be comfortable wearing multiple hats. I have performed cross-functional roles such as data scientist, ML engineer, and product manager, which has given me a holistic understanding of the entire Machine Learning product development lifecycle.

How did you get interested in data science?

I became interested in Data and Analytics while collecting wind energy generation data in one of my previous firms. I got the opportunity to analyze the data back in 2013 and surprisingly detected a significant reduction in the generation of wind energy despite the season being a high wind season. This discovery was later validated by the onsite engineers, who were able to perform preventive maintenance of the turbines and averted a complete machine breakdown. By that point, I was sure that the science of using data to get insights and take action would be extremely beneficial in solving the pressing issues of the world. I knew I needed to learn more about this field and gain experience in order to be able to combine my engineering skills with analytical skills to solve real life industry problems. Subsequently, I decided to break into the field of data science and I taught myself Python, R, and SQL programming languages in the following years through various online courses.

What are you currently working on?

I am currently a graduate student at Carnegie Mellon University, where I am working on building leadership skills to develop and deliver Machine Learning and AI products with real-world impact, ROI, and customer experience. In addition to this I am also working actively to build a community of open source data science projects that will try to bridge the gap between learned skills and employable skills in data science.

How did you first discover WiDS?

I discovered WiDS in the year 2018 through a Kaggle competition and it was my first competition on the platform.

Have you been involved with WiDS since that first experience? If so, in what way?

Yes, after my first competition, I continued to participate in subsequent WiDS datathons on Kaggle in the year 2022 and 2023. As and when time permitted, I tried to work on exploring the WiDS datasets on energy consumption or weather prediction and also made some basic predictions and submitted them to the competition. I really like the format of WiDS datathons where the participants are working on anonymized real-life datasets to solve some of the pressing issues of the world such as climate change and energy conservation. I also make it a point to go through the winning submissions and papers submitted during the competition, as this provides me with a wealth of knowledge on the subject and motivates me to enhance and contribute to my data science and machine learning skills.

How has WiDS made an impact on your life and/or work?

WiDS has definitely impacted my life and work in many different ways. Participating in the WiDS Datathon competitions gave me confidence to participate openly in many other data science competitions on Kaggle. Prior to that I used to have impostor syndrome, where I was hesitant to publicly share my work on platforms like Kaggle and Medium. In fact one of my work on WiDS 2022 dataset regarding Building Energy Efficiency prediction earned me a Kaggle gold medal and that was amazing. In addition to this, I also got tremendous inspiration from so many talented WiDS speakers that led me to submit a presentation proposal to one of the WiDS regional conferences that encouraged first time speakers to participate. Fortunately, my technical talk proposal was accepted, which will launch a new chapter in my career as a speaker, something I never imagined I would be able to do.

What comes next for you? And what are your hopes for women in data science in the future?

As I am currently pursuing a master’s degree, I will continue to work in the field of data science, with a focus on addressing the challenges that data science, machine learning, and artificial intelligence face in providing value to its stakeholders and the society. I want to create ML and AI products that effectively address some of the world’s most pressing issues, including climate change, food security, and sustainability. In my opinion, the field of ML and AI has a lot of potential to tackle major issues in the world that still need to be explored and implemented for the betterment of society, and I strongly believe that women are nurturers and multitaskers with the ability to think multidimensionally to explore solutions to the world’s problems. I am hopeful that more and more women from different walks of life will be encouraged to get into this field through communities like WiDS, so that they can also have this superpower of data science which is an amalgamation of business, computing and statistics.