About
Highly analytical and results-driven Integrated M.Sc. candidate in Computational Statistics and Data Analytics, poised to leverage expertise in Python, R, and advanced ML/AI tools to drive data-driven decision-making. Proven ability to develop robust analytical pipelines, build predictive models (achieving up to 95% accuracy), and extract actionable insights from complex datasets, as demonstrated through impactful projects and an analytics internship. Eager to apply strong statistical foundations and practical data science skills to solve challenging problems and contribute to innovative solutions in a dynamic environment.
Work
Remote, N/A, India
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Summary
As an Analytics Intern, developed and automated robust portfolio analytics pipelines, designed advanced evaluation logic, and engineered reusable modules to generate critical financial insights.
Highlights
Automated portfolio analytics pipelines using Python, generating critical insights from mutual fund data, including eCAS, NAV history, and holdings.
Developed advanced logic for FIFO-based invested value, fund-wise XIRR/CAGR, asset allocation, and market cap splits, enhancing portfolio evaluation accuracy.
Engineered reusable modules to efficiently compute equity sector exposure, risk-return summaries, and performance attribution metrics.
Collaborated cross-functionally to reconcile data with industry benchmark tools, ensuring analytical parity with platforms such as Value Research and BeyondIRR.
Volunteer
Computer Society of India CSI- VIT
|Member of Team Events
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Summary
Actively contributed to the Computer Society of India CSI-VIT, organizing and coordinating technical events and workshops to foster student engagement in technology and data science.
Highlights
Coordinated flagship events 'TechStorm' and 'CodeConnect' with over 250 participants, significantly boosting engagement in coding and software development.
Orchestrated 'CodeX', a 36-hour hackathon involving 180+ participants, focusing on Python, data analytics, and web development.
Delivered workshops on Machine Learning, data visualization, and programming basics, benefiting over 100 students.
Facilitated guest lectures and industry sessions, enhancing academic learning with practical, real-world insights.
Education
Skills
Programming Languages
Python, R, SQL, C++, Java, HTML, CSS.
Data Science & Machine Learning
Machine Learning, Deep Learning, Statistical Modeling, Predictive Modeling, Data Analysis, Collaborative Filtering, Logistic Regression, Oversampling, Feature Engineering, Model Evaluation, Data Preprocessing, Data Quality, Performance Attribution.
Frameworks & Libraries
Pandas, Numpy, Pytorch, Scikit-learn, TensorFlow, SciPy, Seaborn, Matplotlib.
Tools & Platforms
Git, Jupyter Notebook, Microsoft Excel, Canva, SPSS, Power BI.
Analytical & Business Skills
Portfolio Analytics, Financial Analysis, Cross-functional Collaboration, Problem Solving, Strategic Planning, Data Visualization, Reporting.