Shubhi Bakliwal
Compensation & Strategic Finance
Where compensation meets strategy.I build what makes it scale.
Six+ years in Compensation & Strategic Finance at Palantir — from pre-IPO through the 2020 direct listing to public-company scale, now supporting a global workforce of 4,500+.
Comfortable at every altitude — from board-level stock-based compensation analysis down to a single edge-case pay decision.
Compensation at scale, from strategy through execution
At Palantir, I am an integral part of compensation strategy, workforce planning, and the financial analysis behind them across a 4,500+ person global workforce — partnering with FP&A, legal, mobility, recruiters, payroll, finance partners, and business leaders on the decisions that shape headcount and spend. I help drive forecasting, budgeting, and planning for base pay, bonus, and equity, and turn that work into clear decision support for leadership on compensation, hiring, and attrition. That includes board and executive materials, benchmarking and leveling work grounded in Radford, Levels.fyi, and internal market data, and bringing structure to pay decisions that are often nuanced, cross-functional, and genuinely ambiguous.
I build the systems that run it
I build systems that make compensation work more scalable and more consistent. At Palantir, that has included Foundry-based dashboards and workflows for equity queues, pay equity, and employee compensation messaging, along with the data and interface layers that support them. I also bring a practical understanding of the broader systems landscape around compensation, including tools like Workday, Shareworks, and NetSuite. That matters because strong compensation work depends not only on analysis, but on how well information moves across fragmented systems and teams. My focus has been to make that chain more reliable: better structure underneath the data, smoother handoffs across workflows, and cycle times shortened from months to days.
Analytical judgment, accelerated
I use AI where it genuinely improves the quality and speed of the work — especially in analysis, iteration, and validation. The value is not only the tool itself, but the ability to explore more paths, test assumptions faster, and arrive at a sharper recommendation while keeping context, trade-offs, and judgment front and center. In practice, that means using LLMs to accelerate analytical code, pressure-test scenarios, and refine thinking faster, while staying accountable for the quality of the answer.
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.