About the company
CAIS is the pioneer in democratizing access to and education about alternative investments and structured investment solutions for independent financial advisors, empowering them to engage and transact with leading asset managers on a massive scale through a wide variety of alternative investment products and technology solutions. CAIS provides financial advisors with access to a broad range of alternative investment strategies, including structured investments, hedge funds, private equity, private credit, real estate, and digital assets. CAIS also delivers industry-leading technology, operational efficiency, and world-class client service throughout the pre-trade, trade, and post-trade experience. CAIS supports over 50,000 advisors who oversee more than $6 trillion in network assets.
Job Summary
Responsibilities
📍Develop models leveraging features sourced from structured and unstructured data.
📍Design and develop models for portfolio optimization, recommendation systems, propensity models, lead scoring, time series forecasting, and risk analysis using a combination of classical statistical methods, machine learning algorithms and novel deep learning algorithms.
📍Write modular, production-grade code for model development, data pipelines, and deployment. Prototype user demos rapidly to gather stakeholder feedback and iterate on solutions.
📍Build scalable systems to evaluate, calibrate and iteratively evolve the models in response to changing economic and investment conditions.
📍Ensure rigorous testing with carefully crafted end-to-end and unit test cases for models and related sub-components.
📍Prepare structured and unstructured data to use as features for maximum model performance.
📍Deploy and monitor models in a cloud environment, prioritizing scalability, low latency, and A/B testing methodologies.
📍Stay at the forefront of AI advancements, continuously researching and applying the latest in deep learning and machine learning techniques.
What You Bring
📍Proven expertise in Python programming, with deep knowledge of data structures and algorithms.
📍Excellent command over statistical reasoning.
📍In-depth understanding of predictive modeling techniques, time series analysis, anomaly detection, and clustering
📍Proficiency with data visualization, statistical modeling and data analysis frameworks such as scikit-learn, SciPy and matplotlib.
📍Hands-on experience with Pytorch and deep learning model architectures, such as Transformers, VAE, state space and diffusion models.
📍Experience in fine tuning models using LoRA or similar methods.
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