Machine Learning Scientist - Leading Quantitative Investment Fund - New York - TC up to MM USD

Machine Learning Scientist

• Leading Quantitative Investment Fund
• New York
• TC up to MM USD at Hunter Bond Machine Learning Scientist
• Leading Quantitative Investment Fund
• New York
• TC up to MM USD at Hunter Bond in West New York, New Jersey Posted in 4 days ago.

Type:

full-time

Job Description:

Job title: Machine Learning Scientist (Quantitative Strategies)

Salary:

Up to $500,000 starting base + industry-leading guaranteed bonus and package.

Location:

New York

• Hybrid (Up to 3days onsite per week).

Client:

Globally leading quantitative investment manager and an early pioneer of systematic strategies in the 1990s. Scientific and data-driven fund who have been at the forefront of computational finance for the last four decades. Multi-disciplinary team of STEM-subject matter experts who deploy a collaborative approach to developing and executing quantitative and fundamental trading strategies across an array of asset classes and investment products.

Role:

Following stellar performance in financial markets last year, this firm are scaling their quantitative research and trading capabilities in New York. Given longstanding investments in ML, they're in the process of developing and deploying the firm's first suite of ML-driven strategies. Seeking to apply a multitude of novel ML methodologies from LLMs, DNNs, CV to more traditional frameworks in Statistical ML to an array of complex and noisy data. Role entails holistically contributing to the research lifecycle including alpha signal generation, understanding risk and maximizing profitability of quantitative trading strategies.

Required skills:

Advanced degree in STEM or another highly quantitative discipline. Proficient knowledge of novel areas of ML including but not limited to GenAI, LLMs, DNNs, CV, Agentic AI and more traditional forms like Statistical ML. Capable across topics in algorithms, data structures, probability and statistical analysis. Extensive experience of dealing with noisy data challenges. Proficiency in either Python or C++.

Desirables:

Strong ML-related publications in top conferences or peer-reviewed journals including ICML, NeurIPS, AISTATS and CVPR. Best Paper Awards or Impact accreditations across ML. Direct experience of translating mathematical models to production-level code. If this opportunity is of interest, please apply direct or email me at asalim@hunterbond.com .