Sonali Baskaran
Software Engineer at Multicoreware
Based in India
About
My software engineering journey ignited a passion for data. Now, equipped with a Merit Award-winning Business Analytics degree from IIM Trichy, I'm transforming from a code builder to a data enthusiast. I thrive on extracting meaning from information, weaving data-driven narratives, and collaborating with teams to unlock the potential of data for impactful business outcomes.
Skills
Projects
Experience
June 2022 - Present
Software Engineer at Multicoreware
Applied expertise in machine learning and AI to enhance image and object detection algorithms in autonomous vehicle systems, contributing to safer and more efficient navigation. Led a team of neural network specialists in optimization efforts, achieving a 90% efficiency improvement in deep learning models, boosting computational power and resource utilization. Fostered a culture of communication and collaboration across data science, product, and development teams, ensuring successful project delivery and exceeding team goals.
October 2020 - June 2022
Data Analyst at Cognizant
Leveraged machine learning and statistical models to analyze customer purchase patterns, identifying key buying trends and predicting future market demand with 85% accuracy. Designed a strategic plan for email automation using Python, reducing manual effort by 50%. Automated email attachment classification based on file format, reducing processing time by 25%. Collaborated cross-functionally with business stakeholders to translate data insights into actionable recommendations, driving a 10% revenue increase in Q3 2023.
Education
April 2022 - Oct 2023
Indian Institute of Management Trichy
Merit Award Winner Capstone Projects: Smartphone and Accessories Dataset: ● Built a sales forecasting model with an 85% accuracy rate. ● Conducted market basket analysis, increasing cross-selling opportunities by 15%. Customer Classification and Prediction using Loan Dataset: ● Developed a model using multinomial and K-means cluster analysis to identify at-risk customers, contributing to risk management.
2016 - 2020
BE Computer Science Engineering at St. Joseph's College of Engineering
Project: Facial Emotion Recognition using CNN: ● Achieved a 95% accuracy rate and an average processing time of 0.1 seconds.