Summary

As a Machine Learning Specialist / Data Scientist, you will play a pivotal role in shaping the future of predictive modeling within the alternative asset management and wealth management space. Your expertise will influence product vision discussions, drive data-informed decisions, and enhance the intelligence of our platform. You’ll collaborate across teams to develop scalable, robust models and frameworks for adoption across products that empower financial advisors and asset managers to navigate complex markets with confidence. 

Responsibilities

1. Develop models leveraging features sourced from structured and unstructured data. 
2. 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. 
3. Write modular, production-grade code for model development, data pipelines, and deployment. Prototype user demos rapidly to gather stakeholder feedback and iterate on solutions.  
4. Build scalable systems to evaluate, calibrate and iteratively evolve the models in response to changing economic and investment conditions.  
5. Ensure rigorous testing with carefully crafted end-to-end and unit test cases for models and related sub-components. 
6. Prepare structured and unstructured data to use as features for maximum model performance.  
7. Deploy and monitor models in a cloud environment, prioritizing scalability, low latency, and A/B testing methodologies.  
8. Stay at the forefront of AI advancements, continuously researching and applying the latest in deep learning and machine learning techniques.  

What You Bring

1. Proven expertise in Python programming, with deep knowledge of data structures and algorithms. 
2. Excellent command over statistical reasoning.  
3. In-depth understanding of predictive modeling techniques, time series analysis, anomaly detection, and clustering
4. Proficiency with data visualization, statistical modeling and data analysis frameworks such as scikit-learn, SciPy and matplotlib. 
5. Hands-on experience with Pytorch and deep learning model architectures, such as Transformers, VAE, state space and diffusion models. 
6. Experience in fine tuning models using LoRA or similar methods. 
7. Experience in model testing, optimization and feature engineering, with the ability to source and integrate diverse data sets to improve performance.. 
8. Cloud deployment expertise, including Kubernetes, Docker and/or cloud ML platforms such as Amazon SageMaker. 
9. Exceptional attention to code quality and emphasis on adhering to established software design patterns. 
10. 4+ years of hands-on experience developing and deploying production-grade ML models in one or more of the above areas. 
11. Experience in the financial services industry, specifically investment management, is a huge plus.  
12. MS in Mathematics, Statistics, Data Science, Physics or a related quantitative field.  
13. 5 years of professional experience in workplace setting.

CAIS’ compensation package includes a market competitive salary, a performance bonus, and exceptional benefits. If you are located in New York, New York, the base salary range for this role is $180,000- $230,000. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific office location. 

Skills

• Development
• Machine Learning
• Python
• Software Architecture
• Software Engineering