Data Analyst with 8+ months of experience in financial time-series analysis, Python automation, and KPI reporting.
Skilled in building analytical frameworks and deriving actionable insights from large-scale datasets. NIS…
Data Analyst with 8+ months of experience in financial time-series analysis, Python automation, and KPI reporting.
Skilled in building analytical frameworks and deriving actionable insights from large-scale datasets. NISM VIII certified.
Seeking data/research analyst roles in fintech, BFSI, or analytics-driven organizations.
<p>Performed financial time-series analysis on real-time and historical datasets using Python and API pipelines, supporting daily research and investment decision-making<br>
Built automated reporting systems, reducing manual analysis effort by ∼35% and improving team turnaround time<br>
Designed rule-based analytical frameworks for performance evaluation and risk assessment across multiple financial instruments</p>
<p>Performed financial time-series analysis on real-time and historical datasets using Python and API pipelines, supporting daily research and investment decision-making, Built automated reporting systems, reducing manual analysis effort by ∼35% and improving team turnaround time, Designed rule-based analytical frameworks for performance evaluation and risk assessment across multiple financial instruments</p>
<p>Developed an AI-powered personalized learning platform for underprivileged children using IBM Watson NLP, improving content accessibility for 100+ users<br>
Implemented adaptive content recommendation logic using IBM Watson Assistant, increasing content personalization accuracy by ∼25% over a static baseline</p>
<p>Developed an AI-powered personalized learning platform for underprivileged children using IBM Watson NLP, improving content accessibility for 100+ users, Implemented adaptive content recommendation logic using IBM Watson Assistant, increasing content personalization accuracy by ∼25% over a static baseline</p>
<p>Built a modular Python backtesting engine processing 15+ years of minute-level OHLCV financial data; automated KPI generation (win rate, max drawdown, rolling Sharpe ratio), cutting manual effort by ∼30%.</p><p></p><p>Technologies: Python, Pandas, NumPy, REST API Integration </p>
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