Chirag Bagle
AVP (Gas & Lng) Quant, Infrastructure Modeling, Credit Risk & Trade Strategy @Worley
Signup · Get unlimited contacts
WORK HISTORY
AVP (Gas & Lng) Quant, Infrastructure Modeling, Credit Risk & Trade Strategy @Worley
Led LNG terminal investment and market risk analytics for clients including QatarEnergy and JERA, building dynamic infrastructure models that integrated Bloomberg Terminal datasets (LNGM, TRAF, BMAP).* Developed Monte Carlo, VaR, and netback simulators across JKM, TTF, HH; unlocked $52M in seasonal arbitrage, cut Panama freight exposure −30%, and secured $1.1B terminal approvals with 33% tighter confidence bands.* Built trade flow scenario models across 12 import terminals and 6 liquefaction hubs using Bloomberg TRAF and global shipping AIS datasets, reducing counterparty Credit-at-Risk (CaR) by 27% via early identification of delivery shortfall exposures.* Quantified CAPEX/OPEX risk for 3 proposed terminals (totaling $1.1B) across Europe and Asia-Pacific, validated WACC via Bloomberg WEI function and applied stochastic sensitivity to long-run regas pricing scenarios to deliver board-approved investment-grade financial models with 33% tighter VaR-style output confidence bands.* Delivered LNG pricing sensitivity briefings to senior strategy teams at ADNOC and BP, integrating volatility spreads, slope risk, freight exposures, and carbon-policy signals.* Built automated real-time gas and risk dashboards using Tableau integrated with Python APIs and Bloomberg Terminal to deliver live tracking of basis spreads, exposures by hub/index, monitor LNG portfolio VaR, slope sensitivity, LNG vessel ETAs, storage inventory, and gas burn levels, reducing market response time by 30% for active traders and improved margin-at-risk (MaR) tracking.* Valued long-term SPA portfolio optionality across take-or-pay, swing options, indexation/re-indexation clauses, diversion rights, and embedded caps/floors, quantified liquidity-adjusted VaR (Value-at-Risk) and slope gamma exposure under varying hub spreads. Produced MtM sensitivities that enabled traders to hedge slope and freight deltas, directly protecting PnL from $15M+ downside swings.
EDUCATION
Imperial College London
Master of Science - MS, Process Systems Engineering
Indian Institute of Technology, Madras
Bachelor of Technology - BTech, Chemical Engineering
University of Cambridge
Master of Arts - MA, Public Policy Analysis
ABOUT CHIRAG BAGLE
Quantitative Gas & LNG Market Risk Advisor with 6+ years of global experience across natural gas, LNG, and upstream value chains, delivering trader-ready insights, portfolio risk modeling, commercial strategy, financial due diligence, Pre/Post deals and transaction advisory, Mergers & Acquisitions, operational excellence, digital transformation, and decarbonization strategies across upstream, midstream, and energy transition sectors. Proven track record advising on LNG arbitrage, netback simulation, pricing volatility, capacity forecasting, and gas-to-power transition across Europe, the Middle East, Asia-Pacific, and North America. Built and led valuation models, scenario frameworks, and market forecasts that influenced over $750M in investment and safeguarded portfolios under volatile fundamentals. Deep expertise in SPA structuring (take-or-pay, swing options, indexation/re-indexation clauses), liquidity-adjusted risk modeling, and Monte Carlo stress-testing to quantify VaR/CaR/CFaR/MaR, L-VaR, and PnL exposure. Skilled in Bloomberg Terminal workflows (LNGM, TRAF, WEI, GP), Python, SQL, and real-time dashboarding to translate complexity into actionable strategies that inform capital allocation, hedge design, trading desk decisions, and global energy transition pathways.Led executive advisory engagements for global energy firms, designing predictive maintenance frameworks, CAPEX efficiency models, and data-driven asset reliability strategies that improved operational resilience and reduced capital risk exposure by 30%. Trusted advisor to C-suite executives, policymakers, and asset management teams, ensuring long-term capability-building and sustainable performance improvements.Recognized thought leader in AI-based energy transition frameworks, and industrial decarbonization, shaping global best practices through whitepapers, keynote presentations, and executive workshops at 10+ international conferences. Passionate about embedding data-driven insights, optimizing production economics, and enabling strategic transformations in the evolving energy landscape.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.