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    Senior Quantitative Researcher - New York, United States - Alldus

    Alldus
    Alldus New York, United States

    Found in: Lensa US 4 C2 - 1 week ago

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    Description


    We are a leading multi-strategy hedge fund at the forefront of applying cutting-edge machine learning techniques to quantitative trading strategies.

    With a focus on innovation and technology-driven investment approaches, we leverage data science and artificial intelligence to generate alpha.

    We are seeking a talented AI/ML Researcher to join our dynamic team and drive the development of advanced machine learning models to guide trading strategies.

    While experience in finance is not required, candidates must demonstrate expertise in large language models and a strong ability to apply machine learning methodologies to complex, high-volume datasets.


    As an AI/ML Researcher specializing in quantitative trading strategies, you will play a key role in researching, designing, and implementing innovative machine learning models to identify and capitalize on market inefficiencies.

    You will collaborate closely with quantitative analysts, traders, and software engineers to develop robust trading algorithms and optimize portfolio performance.


    Key Responsibilities:


    Conduct research and experimentation to develop novel machine learning models for quantitative trading strategies across various asset classes and time horizons.

    Utilize large language models and advanced deep learning techniques to analyze unstructured financial data, alternative data, etc.

    Design and implement algorithms for alpha signal generation, portfolio optimization, and risk management, leveraging both supervised and unsupervised learning approaches.

    Collaborate with quantitative analysts to integrate machine learning models into the firm's trading infrastructure, ensuring scalability, reliability, and performance in high-frequency trading environments.

    Conduct thorough backtesting and simulation analyses to assess the efficacy and robustness of developed strategies, identifying opportunities for refinement and improvement.

    Stay abreast of the latest advancements in machine learning, natural language processing, and quantitative finance, and apply cutting-edge techniques to enhance the firm's trading capabilities.

    Communicate research findings, model performance, and trading insights to stakeholders across the organization, including senior management and investment teams.


    Qualifications :
    Master's or Ph.
    D. degree in Computer Science, Statistics, Mathematics, or related quantitative field.

    Minimum of 3+ years of experience in machine learning research and development, with a focus on large language models and high-volume, data-intensive applications.

    Strong proficiency in programming languages such as Python, and experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

    Solid understanding of quantitative finance concepts and familiarity with financial markets, though direct experience in finance is not required.

    Demonstrated track record of developing and implementing machine learning models in real-world applications, preferably in the context of quantitative trading or algorithmic trading.

    Excellent analytical and problem-solving skills, with the ability to thrive in a fast-paced, collaborative environment.
    Strong communication skills with the ability to convey complex technical concepts to non-technical stakeholders.

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