Shreya Singh
About Shreya Singh
Shreya Singh is a Risk Analyst currently employed at Xceedance in Noida, Uttar Pradesh, India. She has a strong background in mathematics and data analysis, with experience in machine learning and predictive modeling.
Current Role at Xceedance
Shreya Singh currently serves as a Risk Analyst at Xceedance, a role she has held since 2023. Based in Noida, Uttar Pradesh, India, she applies her analytical skills to assess and mitigate risks within the organization. Her responsibilities include utilizing data-driven insights to inform decision-making processes and enhance operational efficiency.
Previous Experience at My Analytics School
In 2022, Shreya Singh worked as a Data Analyst at My Analytics School for one month. During her time there, she conducted a SWOT analysis and performed in-depth exploratory data analysis on Big Mart sales data. This experience contributed to her foundational skills in data analysis and reinforced her interest in the field.
Educational Background in Mathematics and Operational Research
Shreya Singh completed her Bachelor of Science in Mathematics at Lady Shri Ram College for Women from 2016 to 2019. She furthered her education at Delhi University, where she studied Mathematical Science and achieved a Master's in Operational Research from 2021 to 2023. This academic background provided her with a strong foundation in quantitative analysis and problem-solving.
Data Science Training and Skills
Shreya Singh has completed a Data Science Bootcamp using Python from Step Up Workshop and has taken multiple Coursera courses focused on data analysis. She is proficient in various technical tools and languages, including Python, SQL, Jupyter Notebook, Machine Learning, and Tableau BI. Additionally, she has a strong comfort level with MS Excel, which enhances her data manipulation and analysis capabilities.
Achievements in Predictive Modeling
Shreya Singh has demonstrated her analytical capabilities through various projects. She achieved an accuracy of up to 94% in predicting customer churn in the telecom sector using a classification model. Additionally, during her internship, she successfully predicted sales for 1559 products across 10 stores by applying machine learning models. She also implemented models like Logistic Regression, Decision Tree, and Random Forest to detect loan defaulters, achieving an accuracy of up to 90%.