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Master's Thesis - Fund and Manager Research - Job Opportunity at PICTET

Geneva, Switzerland
Internship
Entry-level
Posted: June 16, 2025
On-site
CHF 1,500-2,500 per month for a Master's level internship in Geneva's financial sector, reflecting Switzerland's high cost of living and Pictet's premium market position. This range is competitive for Swiss financial services internships and includes potential for performance-based adjustments based on thesis quality and contribution to ongoing research projects.

Benefits

Opportunity to work with one of the world's leading independent wealth and asset managers with over 200 years of Swiss banking heritage
Exposure to global investment expertise across 27 offices in financial centers worldwide
Hands-on experience with proprietary ETF scoring algorithms and quantitative analysis tools
Collaboration with experienced long-only fund analysts providing mentorship and industry insights
Access to comprehensive historical market data and advanced analytical tools for research purposes
Professional development in cutting-edge areas of ETF analysis including synthetic passive investments
Integration into a partner-owned financial services environment emphasizing long-term client relationships

Key Responsibilities

Drive strategic assessment of existing ETF scoring model effectiveness across equity and fixed income markets, directly impacting investment decision-making processes for high-net-worth clients
Lead innovative research to identify and validate new risk factors with significant explanatory power for manager performance, contributing to competitive advantage in ETF analysis
Execute algorithmic enhancement by integrating newly identified risk factors into proprietary scoring models, improving accuracy and relevance for current market conditions
Conduct comprehensive analysis of synthetic passive investment risks and opportunities, developing insights that will shape future investment strategies and client advisory services
Develop predictive insights into manager performance across various investment contexts and time horizons, supporting wealth management decisions for sophisticated investors
Create quantitative algorithms to screen investment universes and identify critical risk factors influencing ETF performance in diverse market conditions

Requirements

Education

Master student in Finance/Economics, Statistics or Datascience

Experience

Student level

Required Skills

A definite interest in financial markets, empirical finance, data analytics and data visualization Proficient in the usual IT tools (MS Office, Tableau, SQL, VBA; R, Python is a plus) A strong communication skills, great autonomy while being a team player Strong organizational skills, attention to details, proactive and solution-oriented mind-set Ability to judge, analyze and work independently Perfect command of French and English
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Sauge AI Market Intelligence

Industry Trends

The ETF market continues experiencing explosive growth with assets under management exceeding $10 trillion globally, driven by investor demand for cost-effective, transparent investment vehicles. This growth has created increased sophistication in ETF analysis and risk management, particularly around active ETFs which have gained significant traction since regulatory changes enabled their broader adoption. The distinction between active and passive ETF strategies has become increasingly blurred, requiring more nuanced analytical approaches. Synthetic ETFs and swap-based structures have gained prominence in European markets, particularly for accessing emerging markets and alternative asset classes where physical replication is challenging or costly. However, regulatory scrutiny around counterparty risk and transparency has intensified, making sophisticated risk analysis capabilities increasingly valuable for institutional investors and wealth managers. Political instability and currency volatility have become primary concerns for global ETF investors, particularly following geopolitical events, Brexit implications, and emerging market currency crises. This has elevated the importance of quantitative risk models that can effectively capture and predict these macro-level risk factors in real-time portfolio management decisions.

Role Significance

The candidate will likely work within a specialized research team of 5-8 professionals, including senior fund analysts, quantitative researchers, and portfolio managers. This structure provides mentorship opportunities while allowing for independent research contribution and direct interaction with investment decision-makers.
This represents a high-impact entry-level research position with significant autonomy and direct contribution to proprietary investment models. The role carries substantial responsibility for algorithm development and risk factor identification, positioning it above typical internship roles in terms of strategic importance and technical complexity.

Key Projects

Development and validation of proprietary ETF scoring algorithms incorporating multiple risk factors across global markets Historical backtesting and performance attribution analysis for active versus passive ETF strategies Synthetic ETF risk assessment including counterparty exposure, tracking error analysis, and liquidity risk evaluation Cross-market risk factor identification and integration into existing quantitative models Manager performance prediction modeling across various market conditions and time horizons

Success Factors

Strong quantitative and analytical capabilities combined with deep understanding of financial markets and ETF structures will be essential for developing meaningful insights and improving existing models. The ability to work with large datasets and complex financial instruments while maintaining attention to detail in risk factor identification will directly impact the quality of research outcomes and model enhancements. Excellent communication skills and collaborative approach will be crucial for working effectively with experienced analysts and presenting research findings to senior investment professionals. The ability to translate complex quantitative insights into actionable investment strategies will demonstrate value-add to the broader investment team and enhance career development opportunities. Proficiency in multiple programming languages and analytical tools, particularly Python and R for quantitative analysis, combined with database management skills for handling large historical datasets. Technical versatility will enable more sophisticated analysis and model development, setting the foundation for advanced career progression in quantitative finance roles.

Market Demand

High demand exists for quantitative finance professionals with ETF expertise, particularly those combining traditional finance knowledge with data science capabilities. The growing complexity of ETF markets and increasing regulatory requirements for sophisticated risk management create strong career prospects for candidates with this specialized skill set.

Important Skills

Critical Skills

Quantitative analysis and statistical modeling capabilities are fundamental for developing meaningful insights into ETF performance and risk factors. The ability to work with large datasets, perform complex calculations, and validate model outputs will directly determine the quality and reliability of research contributions. Proficiency in Python, R, and SQL enables sophisticated data manipulation and analysis that forms the foundation of modern investment research. Deep understanding of financial markets, particularly ETF structures, synthetic instruments, and passive investment strategies, is essential for identifying relevant risk factors and developing appropriate analytical frameworks. Knowledge of regulatory environments, counterparty risks, and market microstructure will inform model development and ensure research outputs meet institutional investment standards. Strong communication and presentation skills are crucial for translating complex quantitative findings into actionable investment insights for senior professionals and clients. The ability to clearly explain methodology, limitations, and implications of research will determine the practical impact and adoption of developed models within the broader investment process.

Beneficial Skills

Experience with alternative data sources and machine learning techniques will enhance the sophistication of risk factor identification and model development. As financial markets increasingly incorporate non-traditional data sources, familiarity with text analysis, sentiment data, and alternative datasets will provide competitive advantages in generating alpha and managing risk. Knowledge of European regulatory frameworks, particularly MiFID II requirements and UCITS regulations affecting ETF structures, will enhance the practical applicability of research outputs. Understanding compliance requirements and regulatory trends will ensure model development aligns with evolving industry standards and client requirements. Familiarity with portfolio management systems and institutional trading platforms will facilitate the practical implementation of research insights into investment processes. Understanding how quantitative models integrate with portfolio construction and risk management systems will enhance the commercial value of analytical contributions.

Unique Aspects

This role offers rare direct access to proprietary quantitative models and algorithms used in managing substantial client assets, providing insights into institutional-quality investment processes typically unavailable at entry-level positions. The opportunity to modify and enhance existing scoring models represents significant responsibility and potential for measurable impact on investment outcomes.
The focus on synthetic ETF analysis and emerging risk factors positions this role at the forefront of evolving investment product development and regulatory changes affecting European markets. This specialized expertise will be increasingly valuable as synthetic structures gain prominence and regulatory frameworks continue evolving.
Working within Pictet's research environment provides exposure to ultra-high-net-worth client requirements and sophisticated investment strategies, offering perspectives on family office services and multi-generational wealth management that extend beyond traditional asset management roles.

Career Growth

Career progression typically occurs within 2-3 years for high-performing candidates, with opportunities for advancement to senior analyst roles within 3-5 years. The specialized nature of ETF analysis and quantitative modeling creates accelerated career paths for professionals who demonstrate strong technical capabilities and market insight.

Potential Next Roles

Junior Quantitative Analyst positions within asset management firms focusing on ETF and passive investment strategies Fund Analyst roles at investment banks or wealth management firms specializing in manager research and due diligence Risk Management Associate positions at institutional investors or hedge funds requiring expertise in alternative investment structures Product Development roles at ETF providers or financial technology companies developing next-generation investment products Portfolio Management trainee positions at multi-family offices or private wealth management firms serving ultra-high-net-worth clients

Company Overview

PICTET

Pictet represents one of Europe's most prestigious independent wealth and asset management firms, distinguished by its partner-owned structure and over two centuries of continuous operation. The firm manages approximately CHF 600 billion in assets globally, focusing primarily on ultra-high-net-worth individuals and institutional clients. Pictet's reputation for conservative, long-term investment approaches combined with innovation in quantitative analysis makes it an ideal environment for developing sophisticated investment research capabilities.

Pictet holds a leading position among European private banks and independent asset managers, consistently ranked among the top global wealth managers by assets under management and client satisfaction. The firm's partner-owned structure provides stability and long-term perspective that differentiates it from publicly traded competitors, while its global presence across 27 offices provides extensive international exposure and career mobility opportunities.
Geneva serves as Pictet's global headquarters and primary research hub, positioning this role at the center of the firm's investment decision-making processes. The location provides access to Switzerland's broader financial ecosystem, including connections to other major banks, asset managers, and financial technology companies that enhance professional networking and career development opportunities.
Pictet emphasizes intellectual rigor, long-term client relationships, and conservative risk management principles that align with Swiss banking traditions. The firm's culture promotes collaboration between experienced professionals and junior staff, creating mentorship opportunities and knowledge transfer that accelerate professional development. The partner-owned structure fosters an entrepreneurial environment where innovative research and analytical contributions are recognized and rewarded.
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