Ryan is seeking a Manager of Data Science to lead and grow a team of Data Scientists and Senior Data Scientists to translate complex tax and financial challenges into proof-of-concepts (POCs) and minimum-viable-products (MVPs) on Azure and Databricks
Requirements
- Expert in Python (pandas, scikit-learn), deep-learning frameworks (PyTorch, TensorFlow, Keras)
- Experience fine-tuning and deploying transformer-based models (e.g. GPT, BERT) via Azure OpenAI or Hugging Face
- Azure Databricks (Spark, Delta Lake, Unity Catalog), Azure Data Factory
- MLflow, Azure ML pipelines, CI/CD (GitHub Actions or Azure DevOps), Docker/Kubernetes
- Terraform/ARM for infrastructure-as-code; monitoring with Azure Monitor or Prometheus
- SQL Server, Azure SQL, Cosmos DB, ADLS/Blob Storage
- Azure Form Recognizer or equivalent for structured/unstructured document extraction
Responsibilities
- Recruit, mentor, and retain a high-performing Data Science team (Data Scientists & Sr. Data Scientists), providing regular coaching, career-path guidance, and structured feedback
- Oversee rapid iteration of POCs and MVPs on Databricks (Spark/Delta Lake) and Azure ML, from feature engineering and model training through evaluation and production deployment
- Lead by example—write, review, and optimize Python notebooks and production code daily; troubleshoot performance bottlenecks and cost-optimize compute
- Architect, develop, and deploy classification models on unstructured data (CNNs, RNNs, transformers) alongside tree-based and ensemble methods for structured data
- Implement end-to-end NLP workflows—tokenization, embedding generation, LLM fine-tuning, and RAG frameworks—for document understanding and virtual assistants
- Rapidly prototype and fine-tune GenAI solutions (Azure OpenAI, Hugging Face), collaborating with Data Engineering and MLOps (MLflow, Azure DevOps) to productionize successful pilots
- Ensure code quality (unit tests, version control, documentation), embed responsible AI practices (bias detection, explainability, data-privacy compliance), monitor model drift, and maintain production SLAs
Other
- Bachelor’s or Master’s in Data Science, Computer Science, Statistics, or related field
- 7+ years of hands-on experience building and deploying ML & deep-learning solutions in production
- 2+ years of managing and mentoring data science professionals
- Proven track record delivering rapid POCs/MVPs and scaling them to robust production services
- Exceptional communicator: translates technical concepts for executive and business audiences
