Data Scientist II
Core
Design, build, and deploy scalable machine learning models and AI solutions to solve complex business problems using large datasets.
Role type
Senior IC data scientist (ML/AI)
Builds
Production ML models, AI/GenAI applications, and automation workflows
Domain
Enterprise technology / Machine Learning / Cloud (Azure)
Deliverable
production ML models
Required skills
Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM), Microsoft Azure services (Azure ML, Databricks, Data Factory, Synapse), supervised and unsupervised learning algorithms, deep learning frameworks (TensorFlow or Pytorch), SQL, MLOps practices (MLflow, CI/CD), data visualization (Power BI, Matplotlib, Seaborn, Plotly)
Preferred skills
Azure certifications (AZ-900, AI-900, DP-100), NLP libraries (Hugging Face, spaCy, NLTK), LLM integrations (Azure OpenAI, LangChain), containerization (Docker, Kubernetes), big data tools (Apache Spark/PySpark), Generative AI (RAG, Prompt Engineering), Git, Agile/Scrum
Technologies
Python, SQL, scikit-learn, XGBoost, TensorFlow, PyTorch, Hugging Face, Azure ML, Azure Databricks, Azure Data Factory, Azure Synapse, Azure OpenAI, MLflow, Azure DevOps, GitHub Actions, Power BI, Pandas, PySpark, Jupyter, Azure Blob Storage, Azure Data Lake, SQL Server, Cosmos DB, Docker, Kubernetes
Responsibilities
Design, build, and evaluate ML models for classification, regression, forecasting, and NLP; Develop and maintain data pipelines for ETL and feature engineering; Deploy ML models on Azure ML using endpoints and pipelines; Collaborate with data engineers to ensure data quality and governance; Apply AI/GenAI capabilities to build intelligent applications; Monitor model performance in production and implement retraining strategies; Translate business requirements into data science problem statements; Participate in code reviews and adhere to MLOps best practices
Seniority
Mid-Senior, hands-on IC
