Quantative Analyst

Company Description

QuantumStreet AI, backed by IBM, is a global leader in AI-driven investment strategies and manages over $7 billion in client assets. Serving prominent banks, asset and wealth managers, and pension funds, QuantumStreet AI specializes in building and deploying AI models for institutional portfolios since 2016. The company provides custom AI-enhanced portfolios, thematic indices, and a robust SaaS platform integrated with Watson for tools like forecasts, signals, and research. With headquarters in San Francisco and a Machine Learning lab in Bangalore, QuantumStreet AI's leadership team brings two decades of expertise in AI and global investment, managing over $100 billion globally.

We are looking for Quantitative Analysts at various levels to join our Investment Science Group. The candidate will analyse complex financial data, create optimised portfolios of assets, build advanced ML models, and deliver insights that drive allocation of multiple billions USD in assets. Candidate will collaborate with cross-functional teams, leveraging cutting-edge tools to improve portfolio performance and risk assessment helping deliver exceptional returns to clients.

Key Responsibilities:

1. Portfolio creation and performance analysis:

Create optimised portfolios for specific client needs. Analyse performance of

portfolios and contribute to client communication.

2. Data Analysis & ML Modelling:

Analyse structured and unstructured financial datasets to extract meaningful

insights. Build and implement predictive models, statistical analyses, and machine learning

solutions for investment optimisation and wealth management strategies. Work on time series forecasting, risk modelling, and portfolio performance analytics.

3. Research & Development:

Optimisation strategies for asset allocation

Conduct exploratory data analysis (EDA) and hypothesis testing

4. Data Engineering & Integration:

Work with large financial datasets, ensuring data quality, cleansing, and integration from

multiple sources (internal systems, market data providers, etc.)