Our Quant/Model Val recruiting team is partnered with the Head of Model Risk with a leading Global organization in search of a Senior Quantitative Analytics Lead that will be focused on bridging the gap between Data Science & AI/ML Research, and Technology & Quantitative Modeling.
You will be joining a diverse team and will be part of one of the worlds leading Model Validation/Quantitative Analytics groups. You will be a key contributor to the development of the organization's next generation model diagnosis and validation system with a focus on AI/ML models. This capability will be the key to scaling up and increasing the frequency of their model validation operations. You will be focused on conducting end-to-end concept design and building functionally complete prototypes, while resolving uncertainty around key design decisions.
Required Qualifications:
- Master's Degree in computer science, electrical engineering, data science, or another discipline with a strong quantitative core and extensive exposure to software engineering or scientific computing
Additional Desired Qualifications:
- PhD in computer science, electrical engineering, data science, or related areas
- 4+ years of hands-on experience and deep knowledge in engineering data- and compute-intensive systems with a track record of success in delivering systems into production
- Systems mindset, with the ability to abstract interfaces, future-proof designs, and anticipate potential problems, as well a keen ability to think in terms of building blocks, spot improvement opportunities and identify creative ways to find innovative solutions for them by remixing prior art in the AI/ML literature
- Broad understanding of common machine learning frameworks, with emphasis on big picture understanding of their internals, key abstractions used, and underlying design decisions, e.g. scikit-learn, pycaret, MLR, h2o, MLlib, MLJ, linfa, MLpack, tensorflow, pytorch. Knowledge of AutoML systems (datarobot, driverless AI, teapot) a big plus.
- An understanding of data science processes and everything that goes into building, testing, and operating a model end-to-end. Knowledge of recent frameworks for Machine Learning pipelines such as MLFlow, Kubeflow, Airflow, and TFX is a big plus
- Broad knowledge of recent developments in AI/ML interpretability, safety, fairness, causality, and adversarial testing, with emphasis on frameworks (e.g. IBM Fairness 360 , IBM Adversarial Robustness Toolbox, IBM AI Explainability 360, IBM Causal Inference 360, DoWhy, CausalML)
- An understanding of distributed computing frameworks (e.g. Spark, Dask, Ray), with deep subject matter expertise in at least one of them
- Strong programming skills in one systems language (e.g. C, C++, Java, Scala, OCaml, Rust, Go), and one specialized language (Python, R, Julia)
- An understanding of data modeling and relational databases. Advanced SQL, including analytical functions and recent additions to the SQL standard.
- An understanding of common frameworks for analytical applications (e.g. R/Shiny, Dash, H2O Wave) highly desired -- especially H2O Wave
- Strong communication and interpersonal skills with the ability to conceptualize and communicate designs and plans, develop partnerships and collaborate with other business and functional areas will be key