Operations- Performance Analytics- AVP

KKR India Asset Finance; Gurugram District; Hybrid; INR 5,500,000 - 9,000,000; Full time

Job summary

KKR India Asset Finance is seeking an AVP to lead the Performance Analytics function within the Data Operations CoE. You will own performance calculation logic, develop models, and oversee a small team of analysts, delivering insights across fund, deal, and investor levels.

You will work on methodology, reconciliation, and reporting, with exposure to credit, private credit, and multiple asset classes. Strong coding and stakeholder communication are essential.

Qualifications

• 7–10 years of experience in performance analytics, investment analytics, or a similar quantitative role within asset management.
• Direct experience in private markets, including fund structures, capital calls and distributions, and valuations.
• Strong ownership of performance calculation logic and methodology, not just consumption of figures.
• Excellent command of private markets metrics (IRR, MOIC, TVPI, DPI, RVPI).
• Advanced Python and SQL skills for production-quality analytical code and data models.

Responsibilities

• Own design, documentation, and maintenance of performance calculation logic across asset classes.
• Produce performance analytics at fund, deal, asset, investor and class levels.
• Reconcile outputs to accounting sources and resolve variances with audit trails.
• Build attribution frameworks and benchmarking for context.
• Deliver automated reporting pipelines for investment teams and senior management.
• Lead a team of ~3 analysts, set coding standards and mentor juniors.
• Partner with data engineering, Fund Accounting and Investor Relations to ensure robust data foundations.

Skills

Leadership

Communication

Mentoring

Analytical thinking

Problem solving

Education

Bachelor's or Master's in a quantitative discipline

CFA/CAIA or comparable credential

Tools

Python

SQL

dbt

Git

Snowflake

Power BI

Sigma

Tableau

Job description

Job Summary

KKR is hiring for an enterprise Data Operations group to collect, manage, and harness the power of data across our diverse business activities. The Data Operations Center of Excellence (CoE) is a cross-functional team dedicated to formulating and driving KKR s enterprise data strategy while also providing the operating leverage required to bring these strategies and frameworks to life.

We are hiring an AVP to lead the Performance Analytics function within the Data Operations CoE. This individual will own how fund, deal, and investor-level performance is modeled, calculated, validated, and reported across KKR s private equity, credit, real assets, and infrastructure strategies. The role is accountable both for the underlying calculation logic, including methodology, mechanics, and documentation, and for the reporting and analysis delivered on top of it to investment teams, senior management, Finance, and Global Client Solutions.

This is a hands-on leadership position. The successful candidate will personally build models and write code while also leading a team of approximately three analysts and associates, setting technical standards, reviewing work, and developing junior talent. We are looking for a practitioner who is credible in the details of performance measurement and equally comfortable explaining a result to a senior stakeholder who wants to know why a number moved. The ideal candidate brings 7 to 10 years of experience in performance analytics, investment analytics, or a closely related quantitative function, with substantial private markets experience. Exposure to credit, particularly private credit, is a strong advantage given the growth of that business.

We are operating in a 4-day in office, 1-day flexible work arrangement.

Responsibilities

• Performance Methodology & Model Ownership: Own the design, documentation, and ongoing maintenance of KKR s performance calculation logic across asset classes, including gross and net IRR, MOIC, TVPI, DPI, RVPI, invested and returned capital, Dollars at Work, and levered versus unlevered return treatment. Define and defend methodology choices, including fee and waterfall mechanics, cash flow timing conventions, FX treatment, and hedged versus unhedged presentation.
• Multi-Level Performance Analytics: Produce and interpret performance at fund, deal, asset, investor, and share class or series level, ensuring results are internally consistent and reconcilable as they roll up and drill down.
• Reconciliation & Data Integrity: Reconcile performance outputs to accounting and fund administrator sources of truth, investigate and resolve variances, and establish controls that catch breaks before they reach stakeholders. Consume administrator and system-calculated figures where appropriate while maintaining independent validation.
• Attribution & Benchmarking: Build attribution frameworks that explain drivers of return and deliver benchmarking and public market equivalent analysis to contextualize fund and strategy performance.
• Credit Performance Analytics: Support credit and private credit strategies with the metrics that discipline requires, including yield, spread, loss and recovery analysis, and average funded balance measurement.
• Reporting & Automation: Deliver recurring and ad hoc performance reporting for investment teams, senior management, Finance, and Investor Relations, including investor-facing material. Replace manual processes with automated, tested, and version-controlled pipelines that are transparent and easy to verify.
• Team Leadership & Mentorship: Lead a team of approximately three analysts and associates. Set analytical and coding standards, review outputs, prioritize workload, and actively develop the technical and domain skills of junior staff.
• Stakeholder Partnership: Serve as the trusted point of contact for performance questions across the firm, translating stakeholder needs into analytical solutions and communicating results and their limitations clearly to both technical and non-technical audiences.
• Cross-Functional Collaboration: Partner with data engineering, technology, Fund Accounting, and Investor Relations to ensure the data foundations, systems, and controls supporting performance analytics are robust and scalable.

Qualifications

• 7 to 10 years of experience in performance analytics, investment analytics, fund reporting, or a comparable quantitative role within asset management
• Direct experience in private markets is required, including a working understanding of fund structures, capital calls and distributions, commitments and unfunded exposure, valuations, and fee and carry mechanics
• Demonstrated ownership of performance calculation logic and methodology, not solely the consumption of pre-calculated figures
• Strong command of the core private markets performance metrics, including gross and net IRR, MOIC, TVPI, DPI, RVPI, invested and returned capital, and Dollars at Work, with the ability to explain how each is constructed and where each can mislead
• Advanced Python and SQL skills, with the ability to build, test, and maintain production-quality analytical code and data models
• Experience reconciling analytical output to accounting or fund administrator data and resolving variances with a documented audit trail
• Experience building reporting and dashboards for senior stakeholders, and comfort presenting figures that will be scrutinized
• Demonstrated experience mentoring, reviewing, or formally managing junior analysts
• Excellent communication skills, with the ability to explain methodology and results to non-technical audiences
• Meticulous attention to detail, paired with a bias toward structured, formula-driven, and independently verifiable work
• Strong cultural fit (teamwork, results-oriented, proactive, and high integrity)

Preferred Qualifications

• Experience supporting AI workloads in financial services or other regulated industries.
• Experience with credit, and private credit in particular, including yield, spread, loss, and recovery analytics
• Experience across multiple asset classes, such as private equity, credit, real assets, and infrastructure
• Bachelor s or master s degree in a quantitative discipline such as finance, economics, engineering, mathematics, statistics, or computer science
• CFA, CAIA, or comparable credential
• Experience with cloud data warehouses (e.g., Snowflake) and BI tools (e.g., Power BI, Sigma, Tableau)
• Familiarity with dbt, Git, and modern analytics engineering practices, including modular transformations and version-controlled, tested data models
• Experience applying AI, machine learning, or large language models to improve analytical and reporting workflows
• Experience supporting investor-facing performance reporting or LP due diligence requests.