Lead Portfolio Manager (buyside)

About the Opportunity

We are seeking a visionary quantitative Lead Portfolio Manager to design, manage, and scale investment strategies that integrate machine learning-driven insights with disciplined trading and portfolio management. This leadership position is responsible for generating alpha, managing risk, and overseeing a team of researchers and analysts.

Responsibilities

• Head trading strategies design, portfolio performance, and risk-adjusted returns.
• Lead strategy development across multiple asset classes and instruments.
• Establish and evolve investment frameworks that leverage ML for alpha generation and risk control.
• Oversee the design and deployment of ML models for predictive analytics, factor discovery, and portfolio construction.
• Identify and test new sources of alpha through ML-driven research.
• Lead a high-performing team of analysts and researchers.
• Apply strong risk discipline to manage exposures, drawdowns, and liquidity constraints.

About You

Qualifications:

• 10+ years of experience in portfolio management, preferably in a hedge fund or multi-asset investment environment.
• Proven track record of generating sustainable alpha and managing institutional-scale portfolios.
• Strong background in machine learning, data science, and quantitative modeling.
• Hands-on technical/programming skills and knowledge of ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Expertise in handling large datasets and modern data pipelines (SQL, cloud platforms, distributed systems).
• Deep understanding of global markets (equities, credit, rates, FX, commodities).

Eligibility

Preferred Experience:

• Prior experience building or scaling ML-driven investment processes.
• Familiarity with alternative datasets and their use in generating differentiated signals.
• Advanced academic qualifications (MSc, PhD, CFA) in a quantitative or financial discipline.

Benefits

What We Offer:

• Significant portfolio responsibility and autonomy in strategy design.
• Competitive compensation with meaningful performance-based incentives.
• Access to world-class data, ML infrastructure, and research support.
• A culture that values innovation, meritocracy, and disciplined risk-taking.
• Remote setting.