Compensation & Strategic Finance
I build the systems that run compensation.
Six years in Compensation & Strategic Finance at Palantir — from pre-IPO through the 2020 direct listing to public-company scale, now across a global workforce of 4,000+.
Comfortable at every altitude — from board-level stock-based compensation analysis down to a single edge-case pay decision.
Compensation at scale, owned end to end
At Palantir I drive compensation strategy, workforce planning, and the financial analysis beneath them across a 4,000+ person global workforce — partnering with business heads, finance, and people teams on the decisions that move headcount and spend. I run the forecasting, budgeting, and planning cycles for base pay, bonus, and equity, and turn them into the analytical backbone of board and executive materials on stock-based compensation, hiring, and attrition. Benchmarking and leveling — Radford, Levels.fyi, internal market data — sit under the pay decisions I support, from the routine to the genuinely ambiguous.
I build the systems that run it
Most compensation partners operate the tools they are handed. I build them. I design and deploy Foundry-based systems and dashboards used across the company — equity queues, pay-equity workflows, employee comp messaging — and engineer the pipelines and interface layers beneath them myself, in PySpark, Python, R, and TypeScript. Strengthening the ontologies across finance, people, and equity data made the whole chain more reliable; automating the manual, multi-step work collapsed planning and equity cycles from weeks or months into days, behind validation and quality controls that hold up in executive review.
AI-native, already
AI is not a line on a someday roadmap for me — it is in the work today. I use LLMs to write and iterate on analytical code across Python, PySpark, and SQL, to pressure-test scenarios, debug, and move recurring analysis and validation faster than a manual pass allows. For a compensation partner today, that is not a novelty; it is fluency in the craft and in the tools reshaping it — the same instincts, pointed at pay.
The full record
For the reader with more than three minutes — the detail beneath the summary.
Palantir Technologies — Compensation & Strategic Finance
- Build and maintain financial models, planning tools, and reporting frameworks for headcount planning, workforce decisions, and resource allocation
- Partner cross-functionally on hiring decisions, workforce-related expense management, and scenario analysis across people-related spend
- Drive forecasting, budgeting, and planning cycles for base pay, bonus, and equity programs
- Support preparation of Board and executive materials with analytical inputs on stock-based compensation, operating expenses, hiring, attrition, and forecast updates
- Provide proactive analysis on hiring, compensation, and budget trends, surfacing risks and trade-offs for forward-looking planning decisions
- Support compensation decision-making through market benchmarking, job leveling, and compensation analysis using Radford, Levels.fyi, and other market data
- Design and deploy Foundry-based systems and dashboards used across the company to manage equity queues, employee messaging workflows, pay equity, and related compensation processes
- Engineer end-to-end internal tooling (data pipelines + UI layers) using PySpark, Python, R, and TypeScript
- Strengthen ontologies and data models across finance, people, and equity systems, improving data consistency, workflow reliability, and downstream reporting quality
- Automate end-to-end compensation and workforce workflows — planning cycles, pay equity, compliance processes, and equity-related operations
- Replace manual, multi-step processes with scalable workflows, enabling faster response to leadership requests, planning changes, and compensation decisions
- Build data validation and quality controls improving the accuracy and consistency of reporting used in compensation planning and executive reviews
- Integrate AI-assisted workflows into day-to-day compensation and financial analysis, using LLMs to generate and iterate on analytical code (Python, PySpark, SQL)
- Use AI tools for scenario analysis, debugging, and rapid iteration, improving turnaround across recurring planning and reporting workflows
Built time-series forecasting and predictive revenue models across every business line, and automated reporting in R that cut multi-day processes to hours. Delivered revenue KPIs and models for private-equity acquisition due diligence, and supported ASC 606 implementation, Workday contract-module testing, and Salesforce data-integration requirements.
Sole data scientist at a solar-optimization startup — built an online analytics platform with diagnostic reporting in R Shiny and R Markdown, and applied statistical and machine-learning methods to predict daily power-mismatch patterns, cutting recurring manual effort by roughly half.
Built a conversational chatbot interface for a hospitality IoT device used by a US luxury brand — in-room dining, concierge, spa and fitness booking — with Dialogflow, PHP/SQL, and Swift; launched at HITEC 2017.
Mukesh Patel School of Technology Management & Engineering.