Senior Data ScientistYou move fluidly between model development and software engineering – including model evaluation, tuning/performance, operationalization, ML demos and visualizations, as well as scaling algorithms. This is neither a pure research role nor an ML ops/engineering position, but you’re comfortable building your own pipelines and partnering with technology to help streamline model deployment.Experience developing a range of models over the course of your professional career. Mastery of foundational/classical machine learning is assumed (regression, random forest, gradient boosting, ensembles, neural networks, feature selection/reduction, parameter tuning, etc.), however, expertise in one of the following ML/CS solution domains will be given preference:NLP/NLG, LLMs, ASR, embedding services/stores, i.e., transformer-based capabilities / architectures and variants (any modality, e.g., text, image, audio); trending open-source model genres, e.g., GPT, FLAN/T5, Llama, BERT, BART, LSTMs, BLOOM, Pythia, DOLLY, ASR, etc.Production recommender systems (Bayesian models, collaborative systems, MABs, and reinforcement learning)Experience writing production-level code using cloud-based environments, pipelines and frameworks, e.g., AWS SageMaker/endpoints, EKS/Kubernetes, Kinesis, MS Azure, GCP and PEP 8 coding standards; prototyping and deploying ML solutions, using experimentation design and GLMs.Experience on projects involving a variety of massive data sources – from relational data to unstructured data such as clickstream, text, images and audio.Expert programming, framework, and technology skills in several of the following: Python, PyTorch, TensorFlow (optional), JAX (optional), Streamlit, Gradio, Spark/PySpark (optional), SQL, Containerization/Docker, Hugging Face, git/git stash.
C/C++/C# experience is a plus, even if you have not used these skills recently.