Machine Learning Researcher (Quant Researcher)
Fintal Partners, New York, NY, United States
Machine Learning Research Lead (Quant Research)
We’re partnered with a top-tier global trading firm that is investing heavily in building out a centralized machine learning ecosystem across its trading business. This is a unique opportunity to step into a leadership role at the intersection of ML research, trading, and infrastructure shaping how machine learning is applied at scale across multiple asset classes.
The role goes beyond pure modeling. You’ll play a key role in defining the research platform, guiding experimentation, and enabling teams to deploy ML-driven strategies in production environments where performance and speed matter.
What You’ll Be Doing
Lead the design, development, and deployment of machine learning models applied to trading and market prediction
Drive research into advanced ML techniques for signal generation, forecasting, and portfolio optimization
Partner closely with traders, researchers, and engineers to translate market intuition into data-driven models and features
Oversee data pipelines, feature engineering, and integration of structured and alternative datasets
Help build and scale a centralized ML research environment used across multiple teams
Define best practices around experimentation, model validation, and productionization
Mentor and guide junior researchers while fostering a strong research-driven culture
What They’re Looking For
Advanced degree (PhD or Master’s) in a quantitative field such as computer science, mathematics, statistics, or engineering
4+ years of experience developing and deploying applied machine learning models
Experience working in performance-critical or real-time environments (trading experience is a plus, not a requirement)
Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX or similar)
Deep understanding of the theoretical foundations behind modern machine learning approaches
Experience working across research and engineering teams to bring models into production
Prior leadership or mentorship experience is a plus
Strong communication skills and ability to operate in a highly collaborative environment
Why This Role
Platform-level impact:
Shape how machine learning is applied across an entire trading organization
Blend of research + leadership:
Stay hands-on while influencing broader technical direction
Real-world impact:
Work on problems where models directly translate to PnL
Collaborative environment:
Tight integration across trading, research, and engineering
Build-mode opportunity:
Help define infrastructure, not just use it
#J-18808-Ljbffr
We’re partnered with a top-tier global trading firm that is investing heavily in building out a centralized machine learning ecosystem across its trading business. This is a unique opportunity to step into a leadership role at the intersection of ML research, trading, and infrastructure shaping how machine learning is applied at scale across multiple asset classes.
The role goes beyond pure modeling. You’ll play a key role in defining the research platform, guiding experimentation, and enabling teams to deploy ML-driven strategies in production environments where performance and speed matter.
What You’ll Be Doing
Lead the design, development, and deployment of machine learning models applied to trading and market prediction
Drive research into advanced ML techniques for signal generation, forecasting, and portfolio optimization
Partner closely with traders, researchers, and engineers to translate market intuition into data-driven models and features
Oversee data pipelines, feature engineering, and integration of structured and alternative datasets
Help build and scale a centralized ML research environment used across multiple teams
Define best practices around experimentation, model validation, and productionization
Mentor and guide junior researchers while fostering a strong research-driven culture
What They’re Looking For
Advanced degree (PhD or Master’s) in a quantitative field such as computer science, mathematics, statistics, or engineering
4+ years of experience developing and deploying applied machine learning models
Experience working in performance-critical or real-time environments (trading experience is a plus, not a requirement)
Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX or similar)
Deep understanding of the theoretical foundations behind modern machine learning approaches
Experience working across research and engineering teams to bring models into production
Prior leadership or mentorship experience is a plus
Strong communication skills and ability to operate in a highly collaborative environment
Why This Role
Platform-level impact:
Shape how machine learning is applied across an entire trading organization
Blend of research + leadership:
Stay hands-on while influencing broader technical direction
Real-world impact:
Work on problems where models directly translate to PnL
Collaborative environment:
Tight integration across trading, research, and engineering
Build-mode opportunity:
Help define infrastructure, not just use it
#J-18808-Ljbffr