Sr Data Scientist

We are looking for a senior data scientist with experience in Graph-RAG Gen AI solution for Natural language understanding and time series models for financial forecasting. Here some of the requirements.

Master's degree in Data Science, Mathematics, Computer Science, Engineering, Information Systems, or related STEM fields. 

3+ years full lifecycle product development and engineering leadership in highly available, highly scalable, and high throughput systems

Experience in deploying out-of-the box LLMs and Generative AI solutions including setting up Vector DBs, Graph RAG and extensive testing of LLMs.

Demonstrated experience with knowledge graphs

Experience in building and owning data science solutions and pipelines within the MLOps ecosystem in Vertex AI. 

Experience in automating, troubleshooting and upgrading data & machine learning pipelines that allow model retraining and continuous monitoring

Hands-on expertise with handling distributed (multi-tiered) systems and automated testing ? unit and performance testing

Demonstrated experience in building and deploying a diverse set of Machine Learning (GLM, GBM, Neural Networks) and NLP solutions at scale 

Proficiency and Hands-on Experience with Git, CI/CD tools, Elasticsearch, and a solution in each category: Orchestration, Container, Model Serving, Observability, and Feature Store

Strong proficiency in programming with Python, Machine Learning libraries and APIs (TensorFlow, Keras, PyTorch, ScikitLearn, H20.ai, XGBoost)

Experience in data visualization and observability with a focus on real time serving and monitoring of time series data with alerts

Strong project management skills and effective stakeholder management skills, coupled with a continuous improvement mindset

Excellent presentation and communication skills, capable of explaining complex technical choices in simple terms to a diverse audience

Experience in one of the following is ideal: NLP on contracts; time series models with cloud spend data

Thorough understanding of enterprise infrastructure technologies (Compute, Storage, Network, Mainframe) to inform model development is preferred