Industry and Academia Roles in Data Science

Akhilesh Chander, Data Science Professional | 08 Mar 2024
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About the speaker

Akhilesh Chander, an accomplished former Data Science Manager at Amazon, boasts over five years of experience in data science. He led a team that crafted innovative models for logistics, including deep learning for image classification. His notable work includes developing forecasting models to predict shipment volumes and utilizing time-series models for accuracy. His expertise in analytics and machine learning fueled service optimization and playbook development for Amazon's global logistics network. Akhilesh is driven by a passion for applying his data science acumen to generate impactful solutions in the logistics industry. He also contributed to optimizing service with supply and demand; shaping and developing supply operating playbooks for events.


In this interactive session, Akhilesh Chander sheds light on the dynamic interplay between industry and academia in the field of data science. His journey from Electrical Engineering to Data Analytics underscores his unwavering commitment to transforming data into strategic assets. Passionate about solving complex business challenges, Akhilesh specializes in analytics and machine learning.

He emphasizes critical data evaluation, extracting meaningful insights, and avoiding superficial conclusions. Akhilesh discusses the art of diverse problem-solving, fostering creativity and adaptability. Balancing technical depth with accessible communication is essential.

Akhilesh advocates for academic excellence as a foundational step. Rigorous learning and research pave the way for impactful industry contributions. Transitioning between academia and industry enriches problem-solving abilities. He also shares how effective communication sets data scientists apart. Conveying complex findings to diverse audiences is crucial. Continual learning and mastery ensure staying ahead.

The Q&A segment provides practical insights for those navigating the exciting data science landscape. Watch the full video for a deeper understanding.

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