Sandeep Yadav
Software Engineer at Twenty Point Nine Five Ventures Pvt Ltd. (20.95)
Noida, Uttar Pradesh, India
Summary
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Senior🎓
Top SchoolSandeep Yadav is a software engineer with nine years of experience building cloud-native fintech and wealth-management systems, currently working at Twenty Point Nine Five Ventures after impactful roles at Syfe and Deloitte. He is fluent in Python, Kotlin, and Java, with a strong focus on algorithms (trees, graphs, DP, backtracking) and applied machine learning, and is exploring a novel blend of metaheuristics and ML. At Deloitte he led small teams to design microservices and rapid prototypes for financial products, and at Syfe he improved build pipelines and test coverage for promo and engagement systems. He also co-authored an IEEE paper on early prediction of employee attrition, reflecting a research-minded approach to practical problems. Based in Noida, he combines hands-on engineering with R&D instincts and a knack for turning algorithmic ideas into production-ready services.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Technology (B.Tech.), Computer Science, Bachelor of Technology (B.Tech.), Computer Science at Jaypee Institute Of Information Technology
English
Github Skills (15)
python10
editor9
vscode9
sorting-algorithms9
vscode-extension9
microsoft9
electron9
typescript9
algorithm9
data-structures9
churn8
visual-studio-code8
visual-studio8
statement7
classification5
Programming languages (3)
Github contributions (5)
sandeepyadav10011995/LLD
Aug 2022 - Jan 2024
Contributions:241 pushes, 1 comment, 1 issue in 1 year 5 months
sandeepyadav10011995/Employee-Attrition-Prediction-Model
Mar 2018 - Apr 2020
Bill Gates was once quoted as saying, "You take away our top 20 employees and we [Microsoft] become a mediocre company". This statement by Bill Gates took our attention to one of the major problems of employee attrition at workplaces. Employee attrition (turnover) causes a significant cost to any organization which may later on effect its overall efficiency. As per CompData Surveys, over the past five years, total turnover has increased from 15.1 percent to 18.5 percent. For any organization, finding a well trained and experienced employee is a complex task, but it’s even more complex to replace such employees. This not only increases the significant Human Resource (HR) cost, but also impacts the market value of an organization. Despite these facts and ground reality, there is little attention to the literature, which has been seeded to many misconceptions between HR and Employees. Therefore, the aim of this paper is to provide a framework for predicting the employee churn by analyzing the employee’s precise behaviors and attributes using classification techniques.
Contributions:17 commits, 2 PRs, 7 pushes in 2 years 1 month
behaviorsaimgatesproblemsmicrosoft
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