I am Bijesh Singha, with a PGPM in Finance and Data Science and experience in B2B AI SaaS solution engineering, product and funnel analytics, technical consulting, and enterprise quality engineering.
Open to Opportunities
Analytics / AI SaaS Solutions / Finance
Enterprise engineering experience
Enterprise test automation & cloud pipeline validationSolutions engineering & client PoCs
Technical demos & PostHog analytics at WebomatesGreat Lakes (Finance & Data Science) / NIT Silchar
Engineering analytical rigor & advanced corporate financeFinance, analytics, and strategy projects
Spanning quantitative models to AI applicationsMy work combines commercial context, analytical structure, and technical execution. I use that combination to shape solutions, understand user behavior, evaluate financial questions, and communicate clear recommendations.
I support the B2B sales process from prospecting and discovery through technical demonstrations, pricing conversations, customized proofs of concept, solution architecture, and consulting. I use Apollo, Nooks, and HubSpot to support outreach, calls, and pipeline coordination.
I track how users discover, enter, and move through AI SaaS products. In PostHog, I analyze acquisition, activation, drop-offs, friction, and conversion, then connect those findings with product improvements and outreach across LinkedIn, social media, and email.
My PGPM work in Finance and Data Science includes corporate valuation, portfolio optimization, fixed-income analysis, scenario modelling, and statistical methods. CFA Level 1 Candidate preparation extends this foundation across investment analysis and capital markets.
A focused set of projects and case studies showing how I frame a problem, choose an analytical or technical approach, and turn the work into a decision or deliverable.
Built an end-to-end valuation and credit assessment model that combines market data, driver-based DCF analysis, scenario logic, and Monte Carlo simulation to estimate a range of values and examine solvency risk.
Built a multi-asset portfolio model that compares allocation choices using expected return, covariance, risk-adjusted performance, backtesting, and tail-risk measures.
Analyzed bond yield, carry, duration, and convexity to construct and evaluate a barbell allocation under different rate and risk conditions.
Mapped acquisition and product events in PostHog to identify where users dropped out of a standalone AI SaaS journey, where friction appeared, and which product or messaging changes should be tested next.
Translated a prospect's testing requirements into a tailored technical demonstration, pricing discussion, proof-of-concept scope, and solution architecture for an AI-enabled Testing as a Service offering.
A progression from hands-on software quality engineering and distributed cloud data systems to client-facing solution architecture, commercial pre-sales, and product growth.
Combining top-tier engineering analytical training with advanced corporate finance, capital markets, and quantitative methods.
Focus on corporate valuation, quantitative portfolio optimization, risk management, predictive analytics, and statistical modeling.
Rigorous quantitative engineering foundation, numerical methods, systems engineering, and data-driven problem solving.
Officially registered for the November 2026 examination. Preparation extends rigorous foundations across financial statement analysis, equity investments, fixed income, quantitative methods, and corporate finance.
A comprehensive toolset combining quantitative capital market modeling, machine learning algorithms, and modern full-stack software architecture.
Long-form notes that explain the reasoning behind the work, including assumptions, trade-offs, evidence, and lessons that can transfer to another problem.
Domestic institutions can absorb foreign selling. That may steady the market. It does not prove that Indian households are getting a good investment.
Why are FIIs selling Indian stocks while DIIs buy? A data-driven analysis of the GDP puzzle, household savings, SIP flows, and why treating domestic savers as a market safety net transfers valuation risk to ordinary families.
I began with a simple idea: improve how two small hotels record bookings and track their work. It soon became a lesson in how complicated a 24-hour business really is.
A personal account of building a hotel operating system for a real hospitality turnaround project in Guwahati. What began as a simple booking app became a lesson in how complicated a 24-hour business really is.
The recruiter brief brings together my target roles, experience, strongest proof points, selected projects, education, custom resume requests, and contact details on one page.
I am open to conversations about financial analytics, product strategy, solution consulting, product analytics, and data-led business roles. Email me or connect on LinkedIn and include the role, team, and location so I can respond with the most relevant work.