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Lead Quantitative Research Analyst

2 months ago


London, Greater London, United Kingdom AGITProp Full time
Position Overview

AGITProp, an innovative firm at the forefront of AI-driven quantitative research, is redefining the landscape of algorithmic trading.

As the complexity and scale of global financial markets evolve, traditional approaches are no longer sufficient. At AGITProp, we are harnessing cutting-edge advancements in artificial intelligence and large language models (LLM) to create groundbreaking models that span various modalities. Our ambitious growth trajectory calls for exceptional talent from both the technology and finance sectors to help us realize our vision.

Role Summary

We are in search of a seasoned and adept Lead Quantitative Research Analyst to join our dynamic team. This role involves the development, implementation, and maintenance of advanced quantitative trading models, strategies, and algorithms. You will collaborate closely with our trading, AI, and engineering teams to ensure the effective integration of your quantitative models into our trading infrastructure. The ideal candidate will have a robust background in quantitative finance, statistics, and programming, with a proven track record of applying complex mathematical models to practical financial market situations.

Key Responsibilities

  • Design, implement, and sustain sophisticated quantitative trading models, strategies, and algorithms, ensuring alignment with the fund's strategic goals.
  • Work in partnership with trading and AI teams to incorporate quantitative models into the trading framework, identifying synergies and opportunities for enhancement.
  • Conduct thorough backtesting and validation of quantitative models to ensure their reliability, precision, and applicability.
  • Examine extensive and intricate financial datasets to uncover patterns, trends, and market inefficiencies that can be leveraged through quantitative strategies.
  • Continuously assess and optimize the performance of deployed quantitative models, making necessary adjustments to maximize returns and mitigate risks.
  • Stay abreast of the latest developments in quantitative finance, machine learning, and related technologies, proactively recommending enhancements and innovations to the team's methodologies and tools.

Qualifications

  • Master's or Ph.D. in Quantitative Finance, Financial Engineering, Applied Mathematics, Statistics, or a related discipline.
  • A minimum of five years of experience in quantitative finance or a similar role, focusing on the development and implementation of quantitative trading models and strategies.
  • Proficient in programming languages such as Python or R, with experience in relevant quantitative finance libraries and frameworks.
  • Comprehensive understanding of financial markets, instruments, and risk management principles, along with the unique challenges associated with quantitative trading.
  • Strong analytical, problem-solving, and statistical skills, with the capability to develop and apply complex mathematical models to real-world financial scenarios.
  • Excellent communication and teamwork abilities, with a proven capacity to collaborate effectively in cross-functional teams.

At AGITProp, we value diversity and believe that it enhances the power of AI, just as it enriches the teams that create it. We are dedicated to fostering an inclusive environment where individuals from all backgrounds can thrive. A diverse workforce leads to broader perspectives, more innovative solutions, and ultimately, superior outcomes.

If you are interested in this opportunity, we encourage you to submit your resume, a brief cover letter, and any relevant publications or research work that may be of interest. Additionally, if this role does not align perfectly with your aspirations but you believe you could contribute value, we invite you to explore other relevant positions within our organization. We have multiple openings and are eager to connect with individuals who possess a background in quantitative finance and AI.