More than 14 years across enterprise SaaS, banking, and financial services, building the planning systems, portfolio governance, and executive cadence leaders decide with. 100% adoption of a new R&D operating model in four months at HashiCorp. $1.6M in incremental 2023 revenue from cloud monetization at Dayforce. A Wells Fargo vendor-model framework that cut deployment SLAs from 18 months to 4.
Programs funded without a scored business case. Roadmaps built on capacity nobody measured. AI tooling bought on vendor-reported uplift. Each one shows up in the quarterly review looking like a strategy problem, and each one is really an operating-model problem. My work sits in that layer: what to prioritize, what to fund, what to pause, and how to tell whether any of it actually worked.
Five numbers from the record, each one a gap between where an operating model started and where it ended up. Switch between the two states, or open a row for the context behind it.
As a founding member of the AI Model Development Center of Excellence, I authored the SR 11-7-aligned Vendor Model Development Lifecycle and trained US and India CoE data scientists on it. Deployment for vendor models dropped from an 18-month SLA to 4. The framework is still in use and influenced how internal models are developed.
Program-managed loan origination automation across underwriting, decisioning, and pricing. For the 70-80% of applications the rules engine decided, underwriting went from one to two days down to seconds.
Ran R&D executive KPI and portfolio reporting for the VP of R&D. Leadership went from reading numbers two weeks old to reading numbers two days old.
Built a forecasting model that reached ~90% adoption across four R&D product groups and was later used for OKR planning. A pilot team cut its estimate variance in half, from 40% to 20%, against a ~10% model target.
GitHub Copilot productivity uplift was reported at roughly 20%. Verified against the data, it came in at roughly 9%. The verified figure, not the reported one, informed a six-figure standardization decision.
My career started in commercial roles in energy trading and liquid logistics in India, then ran through an MBA at Syracuse and into regulated financial services. At Wells Fargo that meant AML product releases, then a $200M+ BCBS 239 data governance program, then a founding seat on the AI Model Development Center of Excellence. Governance, it turned out, is a delivery problem long before it is a policy problem.
At Dodge & Cox, as operating partner to the CTO, I directed prioritization of a $15M technology portfolio run by roughly 200 matrixed staff, led the consolidation of four data groups onto one Snowflake and Azure platform, and oversaw a legacy-retirement plan projecting $3M to $3.5M in cost offsets.
Dayforce added the commercial side: 19 programs governed through intake, FP&A-backed business cases, and KPI scorecards, plus the cloud monetization work behind $1.6M in incremental 2023 revenue and $4.1M in annualized recurring impact. At HashiCorp, an IBM company, the same discipline moved R&D onto a new operating model at full adoption in four months.
Today, through SR Advisory, I build executive decision systems for SaaS and GreenTech clients: OKR and KPI design, QBR and WBR cadences, and risk rubrics. MBA in Finance & Marketing from Syracuse. PMP certified.
A defined-scope contract built around one outcome: a planning and reporting system R&D leadership would actually use. The new Jira operating model reached 100% adoption in four months. The 80+ team change program around it was adopted as the R&D standard and ran about 30% faster than plan in key tracks. The forecasting model reached ~90% adoption across four product groups and was later used for OKR planning.
Roughly 15,000 unbilled service hours, worth over $2M, surfaced in the cloud business. The response was tiered billing across 196 environments, which delivered $1.6M in incremental 2023 revenue and $4.1M in annualized recurring impact. Alongside it: BCG and Finance pricing scenarios modeling $70M to $105M ACV and 4-10% margin uplift, and co-leadership of the ADAM HCM post-merger integration, which unblocked $600K in remediation.
Founding member of the AI Model Development Center of Excellence, covering structured and unstructured AI/ML use cases. Authored the SR 11-7-aligned Vendor Model Development Lifecycle, which cut the vendor-model deployment SLA from 18 months to 4, and trained US and India CoE data scientists on it. The framework is still in use. In parallel, led the Analytics Target Operating Model supporting 10,000+ analysts, backed by a ~$30M modeled three-year ROI case.
Built across Dodge & Cox, Dayforce, HashiCorp, and advisory work. Each move depends on the one before it.
At Dodge & Cox, intake scoring and cross-program decision frameworks set priorities across a $15M technology portfolio. At Dayforce, intake and FP&A-backed business cases decided how 19 programs were prioritized and funded. The point is not the paperwork. It is making the trade-off visible before the money moves.
At Dayforce, the planning model linked the Long-Range Plan to annual execution through quarterly reforecasts, with spend and FTE allocation tracked against the funded plan alongside FP&A. At HashiCorp, capacity forecasting gave R&D leaders a delivery number they could plan against instead of an assumption buried in a roadmap.
Dayforce SLT and operating-committee QBR scorecards went out within 15 days of each earnings call. HashiCorp R&D executive reporting went from a 14-day lag to 2 days. Cadence is what turns a dashboard into a decision.
At HashiCorp, Copilot uplift reported at roughly 20% verified at roughly 9%, and the verified figure informed a six-figure standardization decision. The same discipline sits behind the modeled ROI case for the Wells Fargo Analytics Target Operating Model. Value tracking closes the loop back to intake.