Machine Learning Quantitative Researcher

About the Role

Engineers Gate (EG) is a leading quantitative investment company focused on computer-driven trading in global financial markets. We are a team of researchers, engineers, and financial industry professionals using sophisticated statistical models to analyze data and identify predictive signals to generate superior investment returns. EG’s investment teams each focus on their independent strategies while utilizing the firm’s proprietary, state-of-the-art technology and data platform to optimize their alpha research.

We are seeking a talented and highly motivated Quantitative Researcher with a focus on machine learning to join our dynamic and growing team. The successful candidate will work with the Portfolio Manager to develop and implement the architecture for a machine learning-based mid-frequency equities portfolio. We place a high value on continuous learning and development and this role represents a unique opportunity to work alongside and learn from highly experienced quantitative trading teams.

Joining Engineers Gate offers a unique opportunity to work at the forefront of systematic trading, where innovation and quantitative analysis intersect. We are passionate about implementing scientific and mathematical methods to explore and solve problems in the global financial markets. If you thrive in a fast-paced, data-driven environment, we encourage you to apply.

Key Responsibilities

• Collaborate closely with the Portfolio Manager to design and develop a state-of-the-art machine learning architecture for a forthcoming mid-frequency equities portfolio.
• Implement and optimize machine learning models for trading, focusing on strategies that can provide a competitive edge in the market.
• Drive and lead research initiatives aimed at enhancing the generalization capabilities of our quantitative models in the ever-evolving financial market.
• Explore new methodologies and approaches to stay at the forefront of quantitative finance.
• Develop and enhance a large-scale in-house equity feature pool by incorporating rigorous methods that align with financial intuition and industry best practices.
• Leverage Python programming to develop tools and models that facilitate portfolio management and research processes.
• Take a proactive approach to problem-solving, demonstrating a high level of motivation and initiative in the pursuit of innovative research solutions.
• Collaborate closely with cross-functional teams of researchers, engineers, and financial industry professionals.
• Stay informed about market trends, emerging technologies, and advancements in machine learning.

Required Skills, Qualifications and Experience

• Hold a Master's or Ph.D. degree in a STEM field (Science, Technology, Engineering, Mathematics), or possess a minimum of 3 years of experience in a data science role in technology companies.
• Proficiency in Python programming and strong familiarity with machine learning libraries such as TensorFlow and Keras.
• Up-to-date knowledge of deep learning techniques, including both theory and practical application.
• Demonstrated experience in designing and implementing machine learning models for financial markets is a plus.
• Excellent problem-solving skills and the ability to work collaboratively within a dynamic team environment.
• Solid knowledge of quantitative finance concepts, statistics, and financial markets.

Joining Engineers Gate offers a unique opportunity to work at the forefront of systematic trading research, where innovation and quantitative analysis intersect. We are passionate about implementing scientific and mathematical methods to explore and solve problems in the global financial markets. If you thrive in a fast-paced, data-driven environment, we encourage you to apply.

The salary range for this role is anticipated to be between $130K and $200K. This range does not include any potential bonus amounts, other forms of compensation, or benefits offered. Actual compensation for successful candidates will be carefully determined based on a number of factors, including the candidate’s skills, qualifications, education and experience.