I am Bijesh Singha, an analytical and strategic professional bridging the gap between rigorous data intelligence, corporate finance, and business consulting. With a foundation from NIT Silchar, enterprise experience at LTIMindtree, and an MBA in Finance & Data Science from Great Lakes, I build quantitative valuation engines, advanced data analytics frameworks, and strategic business solutions.
Open to Opportunities
Analytics · Corporate Finance · Consulting
Driver-based DCF, Monte Carlo & VaR Risk Modeling
Predictive Analytics, CV Models & Agentic AI
2+ Yrs Software Solutions & Microservices Validation
GLIM MBA (Finance & Data Science) · NIT Silchar (B.Tech)
Bridging software engineering, institutional finance, and applied artificial intelligence.
Deconstructing complex challenges into resilient, scalable architectures with enterprise-grade validation.
Institutional-grade DCF valuation, 10k-iteration Monte Carlo simulations, and portfolio risk engineering.
Translating advanced machine learning and statistical models into high-impact executive solutions.
A curated portfolio spanning quantitative valuation engines, machine learning frameworks, and enterprise automation architectures.
Engineered an automated, end-to-end corporate valuation and credit risk engine that dynamically calculates probabilistic target prices and assesses structural solvency using live institutional market data.
Engineered a robust, multi-asset portfolio optimization and risk assessment model designed to maximize risk-adjusted returns and strictly quantify tail-risk.
Engineered a risk-neutral fixed income portfolio using a quantitative Barbell Strategy, optimizing for yield (Carry) and Convexity.
Demonstrated track record spanning technical sales engineering, client architecture discussions, and mission-critical software automation for Fortune 500 organizations.
Orchestrate client-facing technical demonstrations and architect customized Proofs of Concept (PoCs)—including a major ETL validation project for MIB—effectively translating complex AI capabilities into high-impact, scalable enterprise solutions that optimize workflows and accelerate strategic business growth.
Partnered with cross-functional teams to integrate PostHog event tracking, analyzing user behavior across key product features to pinpoint funnel drop-offs and orchestrate data-backed platform improvements.
Serve as the strategic liaison for tech executives, facilitating deep-dive architectural discussions and positioning standalone tech assets, such as AIScriptBuddy, to align with long-term client process optimization goals and deliver measurable ROI across their operational ecosystems.
Partner with C-suite leadership and cross-functional teams to manage technical sales pipelines, structure unified product messaging, and drive strategic initiatives that create sustainable business value and position the platform as a preferred technology partner.
Architected and deployed 150+ automated UI and backend test cases for a $50B+ semiconductor leader, validating complex API integrations across distributed microservices to ensure seamless end-to-end data consumption.
Directed comprehensive automation initiatives, ensuring flawless UI-to-database integration by aligning backend workflows with upstream cloud data pipelines, reducing agile delivery discrepancies by 15%.
Standardized end-to-end testing architectures across the enterprise, leveraging root-cause analysis to proactively mitigate deployment risks and accelerate CI/CD pipelines.
Reflections, empirical benchmarks, and system design takeaways from building data pipelines, valuation engines, and machine learning architectures.
A pre-sales engineer's field notes on how agentic AI is splitting enterprise QA into managed services and embeddable tools — and what the research says about where full autonomy still breaks.
From discovery calls with US engineering leaders to the empirical failure modes of autonomous repair loops, here is why enterprise testing is bifurcating into black-box SLAs and CI/CD-native agentic tools.
What I learned building retrieval pipelines during my MBA in Data Science & Finance, and what the 2026 research actually says
Every large language model carries an expiration date baked into it. Should you build complex retrieval pipelines (RAG) or simply dump millions of tokens into massive context windows? Here's what the empirical research actually says about costs, 'Lost in the Middle', and adaptive routers.
A comprehensive toolset combining quantitative capital market modeling, machine learning algorithms, and modern full-stack software architecture.
Dual foundation combining top-tier engineering analytical training with advanced corporate finance, capital markets, and data science.
Mastered the intersection of finance and technology through expertise in quantitative analysis, data science, and financial modeling.
Graduated with distinction. Led the technical committee for the annual tech fest. Research paper published on thermodynamic systems.
Whether you're exploring quantitative valuation models, management consulting opportunities, or data analytics initiatives — let's connect.