In this article, Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021) shares his professional experience as Head of Data Modelling, leading advanced analytics, AI, and reporting solutions for a global investment organisation.

About the company

Tikehau Capital is a global alternative asset management group headquartered in Paris, France. Founded in 2004, it has become a multi-billion-euro investment house with expertise across private debt, real assets, private equity, and capital markets strategies. The company manages assets for institutional and private investors worldwide, relying on a long-term investment philosophy and strong entrepreneurial culture.

Tikehau Capital is listed on Euronext Paris and operates in over a dozen countries. Its diversified investment strategies and robust risk management have contributed to consistent growth and resilience across market cycles.

Logo of Tikehau Capital.

Source: Tikehau Capital.

About the role

As Head of Data Modelling, I was responsible for the strategic design, architecture, and delivery of the organisation’s enterprise-wide analytics infrastructure. My role bridged technology, quantitative modelling, and business strategy, ensuring that investment, risk, and operational teams had access to powerful, automated, and reliable data-driven tools. I oversaw the entire lifecycle of data and analytics projects—from ideation and design to deployment and continuous improvement—while directly coordinating with C-level executives and department heads to align technology solutions with organisational goals.

My Experience

My core responsibility was managing the architecture of our Databricks and PowerBI/Python reporting ecosystem, making it the central platform for the organisation’s portfolio analytics and operational reporting. I led the data strategy for our IT-Quant Cell, which served as a specialised unit delivering high-value analytics to investment and risk teams across multiple asset classes.

One of my most impactful projects was the full-stack development of an AI assistant for answering investor due diligence questionnaires (DDQs). This system combined Databricks Genie with open-webui, enabling internal teams to query complex datasets interactively. Additionally, I built NLP-based solutions to parse and extract information from unstructured documents—such as contracts, company filings, and financial statements—streamlining internal research and reporting workflows.

On the quantitative modelling front, I developed a bond valuation engine capable of pricing both individual securities and portfolios, as well as default probability models for issuers and securities. These tools allowed risk managers to proactively identify watchlist names, foresee covenant breaches, and anticipate coupon defaults. I also delivered full-stack stress testing and credit spread models for private debt and distressed debt portfolios, enabling portfolio managers to assess market scenarios and security-level risks with precision.

For cash flow management, I designed a forecasting engine tailored to private debt portfolios, integrating it with operational and client service functions to automate forecast reporting for investors. I also led the development of large-scale automated reporting solutions capable of generating PDF, PPTX, Word, and Excel outputs, meeting the regulatory and investor requirements of multiple jurisdictions.

Collaboration and Leadership

My role demanded close coordination with the CTO, COO, CFO, department heads, and technical leads to define priorities, allocate resources, and ensure delivery. I managed multi-departmental projects spanning Risk, Investment, Operations, Sales, and Finance, as well as asset class–specific initiatives in Private Debt and Equity, Fixed Income, CLOs, and Real Estate. This cross-functional exposure ensured our solutions were both technically sound and operationally relevant.

Beyond technical delivery, I implemented interactive dashboards for risk monitoring, fundraising, investor onboarding, and portfolio analytics—empowering top management, risk managers, and portfolio managers with actionable insights. I also provided mentorship to analysts and senior executives, guiding them through the adoption of new tools, processes, and workflows.

Required skills and knowledge

This role required deep expertise in data architecture (Databricks, SQL), advanced analytics (Python, NLP, quantitative finance), and visualisation (PowerBI). The ability to translate complex business needs into scalable, maintainable, and user-friendly systems was critical. Equally important were leadership and stakeholder management skills, enabling me to bring together technical and non-technical teams to achieve common objectives.

What I learned

In this position, I learned how to combine cutting-edge technology with robust quantitative frameworks to address the evolving demands of a global investment business. I developed a stronger appreciation for the balance between innovation and operational stability—ensuring that every model, dashboard, or AI system could be trusted by those making high-stakes decisions. Most importantly, I saw firsthand how data strategy, when aligned with business objectives, can transform portfolio monitoring, risk management, and investor communication.

Financial concepts related to my role

Credit spread modelling

Credit spread modelling is the process of estimating the additional yield or premium investors require to compensate for the credit risk of a bond or loan compared to a risk-free benchmark, typically government securities. This spread reflects the market’s perception of the issuer’s default risk, liquidity risk, and other factors affecting creditworthiness. In my role, I built sophisticated credit spread models that integrated multiple layers of data, including macroeconomic variables (such as interest rates, GDP growth, and inflation), issuer-specific fundamentals (like leverage ratios, profitability, and cash flow stability), and real-time market indicators (credit default swap spreads, bond prices, and trading volumes). These models enabled risk managers and portfolio managers to estimate fair value spreads, detect deviations from expected spreads, and identify mispriced securities. The ability to quantify and forecast credit spreads was critical for pricing, risk management, and strategic asset allocation across private debt and distressed debt portfolios.

Stress testing

Stress testing involves evaluating how a portfolio or individual securities would perform under severe but plausible adverse market conditions. It is a key risk management tool that helps identify vulnerabilities and potential losses in extreme scenarios, such as economic recessions, interest rate shocks, or credit market disruptions. I developed full-stack stress testing models that allowed users to apply shocks and scenario analyses both at the individual security level and the aggregated portfolio level. These models incorporated changes in key variables including interest rates, credit spreads, default rates, and macroeconomic indicators. By simulating various stress scenarios, investment and risk teams could assess the resilience of portfolios, anticipate potential covenant breaches or defaults, and plan mitigation strategies. This was especially important for private debt and special opportunities portfolios, where cash flows and valuations can be highly sensitive to changing market environments.

Default probability modelling

Default probability modelling quantifies the likelihood that an issuer or specific security will fail to meet its financial obligations within a defined time horizon. Accurate default prediction is fundamental to credit risk management, pricing, and portfolio construction. I designed models leveraging a combination of financial statement ratios (such as debt coverage, liquidity, and profitability metrics), market-based indicators (equity volatility, credit spreads), and qualitative industry or sector factors to generate forward-looking default probabilities. These models powered watchlists and early-warning systems, enabling portfolio managers to identify issuers at risk of covenant breaches, coupon defaults, or bankruptcy. By anticipating potential defaults, the investment teams could proactively adjust exposures, engage with issuers, or hedge positions, thereby reducing portfolio losses and improving overall risk-adjusted returns.

Why should I be interested in this post?

This post offers valuable insights for students and professionals keen on the intersection of quantitative finance, data architecture, and AI-driven solutions within the asset management industry. It illustrates how leadership in data modelling and technology can directly impact critical investment functions such as portfolio strategy, risk assessment, and investor communications. Understanding how sophisticated models and automated analytics tools are developed and deployed equips aspiring quants, data scientists, and financial engineers with a clearer picture of real-world applications beyond theory—highlighting the importance of cross-functional collaboration, scalable system design, and continuous innovation in today’s complex financial markets.

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Useful resources

Academic articles

Duffie, D., & Singleton, K. J. (2012). Credit Risk: Pricing, Measurement, and Management (illustrated edition). Princeton, NJ: Princeton University Press.

Business resources

Tikehau Capital

Tikehau Capital Solutions

Claessens S., Pazarbasioglu C., Laeven L., Dobler M., Valencia F., Nedelescu O., and Seal K. (2011) Crisis Management and Resolution: Early Lessons from the Financial Crisis, IMF

Preqin Alternative data platform

BlackRock eFront – Portfolio Management Solution

About the author

The article was written in August 2025 by Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021).

In this article, Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021) shares his professional experience as Business & Data Analyst at Tikehau Capital.

About the company

Tikehau Capital is a global alternative asset management group headquartered in Paris, France. Founded in 2004, it has become a multi-billion-euro investment house with expertise across private debt, real assets, private equity, and capital markets strategies. The company manages assets for institutional and private investors worldwide, relying on a long-term investment philosophy and strong entrepreneurial culture.

Tikehau Capital is a public company listed on Euronext Paris and operates in over a dozen countries. Its diversified investment strategies and robust risk management have contributed to consistent growth and resilience across market cycles.

Logo of Tikehau Capital.

Source: Tikehau Capital.

I worked in the Information & Technology (IT) department of Tikehau Capital, and collaborated extensively across various teams within the organisation. My projects focused on building tools and processes that directly supported investment decision-making and portfolio monitoring.

My Experience

As a Business & Data Analyst in the IT department, my role was to design, develop, and deploy technology solutions for the company. I collaborated with several stakeholders such as Investment team, Risk, Operations, Sales and Marketing, Client Services and Top Management. Within the Investment teams I touched on topics across the TKO strategies, Private Debt, Private Equity, Tactical Strategies, Real Assets and Capital Market Strategies. This included working closely with investment professionals to understand their analytical needs and then translating those into scalable, automated systems for data processing, quantitative analysis and reporting.

My missions

As the technical lead and subject matter expert for a major digitisation project in an agile environment, I drove revenue and productivity enhancements through automation, data analysis, and improvements in data quality and reporting. My role involved analysing TKO’s portfolio of private investments, building automated reporting engines, developing quantitative analytics modules for portfolio monitoring, and creating ETL pipelines to consolidate data from multiple internal and external sources. I collaborated extensively across functions—including Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, Investment, Private Debt, Private Equity, and Real Estate—to ensure the successful delivery of technology solutions aligned with business needs.

Key projects included: developing quarterly investor reporting automation tools for several business units (Python, Databricks, Microsoft PowerPoint, Word, Excel); leading the private markets data migration to Databricks and introducing company-wide KPI harmonisation, boosting efficiency by 12x; implementing dashboards for use by both cross-functional teams and top management (Databricks, PowerBI); and automating report delivery to clients (Power Automate). I also led analysts through project lifecycles, providing coaching to both junior and senior team members on in-house and external tools.

From an analytics perspective, I delivered asset performance analyses across funds and asset classes, supported fundraising and investor onboarding analytics, conducted risk assessments on asset performance under economic stress, and developed in-house benchmarking for Private Debt funds in collaboration with external partners. On the investor relations side, I handled institutional investor performance reporting requests. For data quality, I monitored internal platforms, managed escalation processes, and mitigated risks, issues, and dependencies to maintain the integrity and reliability of critical datasets.

Required skills and knowledge

In my role at Tikehau Capital, I developed a combination of technical, analytical, and cross-functional skills that enabled me to deliver technology solutions supporting investment decision-making across multiple asset classes. One of the key technical skills I applied was advanced programming in Python and SQL, which I used to design ETL pipelines, automate reporting processes, and integrate data from multiple sources into Databricks. I also gained deep expertise in dashboard creation and visualisation using PowerBI, allowing me to present complex portfolio performance metrics in a clear and actionable format for both senior management and investment teams.

I strengthened my understanding of private markets data structures, particularly within private debt, private equity, and real estate. This included learning how to manage and standardise KPIs across the organisation to ensure consistency in reporting and analysis. My work required strong knowledge of data governance and quality control, as I was responsible for monitoring internal platforms, managing data integrity issues, and implementing processes to improve accuracy and reliability.

On the soft skills side, I honed my ability to gather business requirements from diverse stakeholders and translate them into technical specifications. This meant working closely with colleagues from Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, and Investment teams in an agile environment. I also developed leadership skills by guiding analysts through the project lifecycle and providing coaching to both junior and senior professionals on the use of in-house and external tools. Finally, I gained significant experience in investor relations support by preparing data for performance reporting, responding to institutional investor requests, and ensuring clear, professional communication of complex investment information.

What I learned

One of the most valuable lessons I learned at Tikehau Capital was how technology teams can act as strategic partners to investment teams across multiple asset classes, including Private Debt, Private Equity, Capital Market Strategies (CMS), Tactical Strategies, and Real Assets. Working in the IT department while collaborating closely with investment specialists taught me how to align technical solutions with diverse investment strategies and operational requirements. For example, during the digitisation project, I learned how to translate complex business requirements from each asset class into scalable automation, analytics, and reporting tools that directly improved portfolio monitoring, decision-making, and investor communication.

I gained a deeper understanding of asset class–specific analytics: in Private Debt and Private Equity, I worked on performance tracking, KPI harmonisation, and risk analytics; in CMS and Tactical Strategies, I learned how derivative positions and macro-driven strategies required different data models and stress-testing frameworks; and in Real Assets, I helped design systems that tracked physical asset performance alongside market and operational metrics. This cross-asset exposure enhanced my ability to adapt technical workflows to varied investment approaches.

From a technical perspective, I refined my proficiency in Python, SQL, Databricks, and PowerBI, using them to build ETL pipelines, automation workflows, and dashboards that served both analysts and senior management. I also honed my problem-solving skills by identifying bottlenecks in reporting processes and implementing solutions that improved efficiency by more than 10x in some areas. Additionally, I learned the importance of outcome evaluation—ensuring that every dataset, whether for an internal management dashboard or an institutional investor report, was accurate, consistent, and presented in a clear, actionable format tailored to the needs of each asset class.

Financial concepts related to my internship

I present below three financial concepts related to my internship and how they were applied in my work at Tikehau Capital: Portfolio monitoring, Credit risk metrics, and Cash flow forecasting.

Portfolio monitoring

Portfolio monitoring is the ongoing process of tracking an investment portfolio’s performance, risk profile, and compliance status to ensure it aligns with its strategic objectives. This involves assessing metrics such as returns, volatility, drawdowns, asset allocation shifts, and adherence to investment guidelines. Effective portfolio monitoring enables timely decision-making, allowing managers to rebalance, hedge, or adjust positions in response to market movements or changes in portfolio objectives. During my time in the IT department working closely with investment specialists across private debt, private equity, CMS strategies, tactical asset allocation, and real assets, I learned how technology can streamline this process. The reporting engines I built automated large parts of the workflow—integrating data from multiple sources, applying valuation models, and generating performance dashboards—allowing investment teams to access accurate, real-time insights without the delays and potential errors of manual data handling. This experience deepened my understanding of how portfolio monitoring supports not only performance measurement but also risk management, regulatory compliance, and informed strategic decision-making.

Credit risk metrics

Credit risk metrics are quantitative and qualitative measures used to evaluate the likelihood that a borrower will default on their obligations and to estimate the potential loss to the portfolio in such an event. These metrics include probability of default (PD), loss given default (LGD), exposure at default (EAD), credit spreads, and internal credit ratings, all of which help investment teams assess both individual counterparty risk and overall portfolio vulnerability. Accurate credit risk assessment is essential for pricing loans, structuring debt instruments, and determining capital allocation. While working closely with investment specialists across private debt, private equity, CMS strategies, tactical strategies, and real assets, I developed tools that integrated multiple credit risk data feeds into interactive dashboards. These systems consolidated financial statement data, market indicators, and external credit ratings into a unified view, enabling faster and more reliable risk assessments. By automating data aggregation and providing visual, real-time insights, these tools not only improved assessment accuracy but also allowed portfolio managers to respond more proactively to emerging credit concerns.

Cash flow forecasting

Cash flow forecasting is the process of estimating the timing and magnitude of future inflows and outflows for an investment or portfolio. It is essential for assessing expected returns, ensuring sufficient liquidity to meet obligations, and supporting capital allocation decisions. Accurate forecasting allows investment teams to anticipate funding needs, optimise debt schedules, and stress test portfolios under different market conditions. This is particularly important in asset classes such as private debt, private equity, CMS strategies, tactical strategies, and real assets, where cash flows can be irregular and heavily dependent on deal structures, economic cycles, and market events. To support this, I built ETL pipelines that extracted deal-level data from multiple internal and external sources, transformed it into a consistent structure, and integrated it with dynamic forecasting models. These pipelines enabled investment teams to perform real-time scenario analyses, adjusting for variables such as interest rate changes, market shocks, and asset performance trends. By automating data preparation and linking it directly to forecasting tools, the process became faster, more transparent, and more adaptable to shifting market conditions.

Why should I be interested in this post?

For ESSEC students interested in finance and technology, this experience shows how a role in IT within an investment firm can offer direct exposure to financial markets, portfolio analytics, and data-driven decision-making—skills highly valuable in both finance and quantitative career paths.

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Useful resources

Academic articles

Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years, Journal of Banking & Finance, 21(11–12):1721–1742.

DeFond, Mark L. & Hung, Mingyi. (2003). An empirical analysis of analysts’ cash flow forecasts, Journal of Accounting and Economics, 35(1):73–100.

Business resources

Tikehau Capital

Tikehau Capital Solutions

Preqin Preqin – Alternative data platform

BlackRock eFront – Portfolio Management Solution

About the author

The article was written in August 2025 by Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021).