Design, develop, and maintain predictive models, decision support tools, and dashboards using Python, R, SQL, Power BI, or similar platforms.
Partner with delivery teams to embed data science outputs into business operations, focusing on improving efficiency, reliability, and end-user experience in Digital Workplace services.
Build and automate data pipelines for data ingestion, cleansing, transformation, and model training using structured and unstructured datasets.
Monitor, maintain, and tune models to ensure accuracy, interpretability, and sustained business impact.
Support efforts to operationalize ML models by working with data engineers and platform teams on integration and automation.
Conduct data exploration, hypothesis testing, and statistical analysis to identify optimization opportunities across services like endpoint health, service desk operations, mobile technology, and collaboration platforms.
Provide ad hoc and recurring data-driven recommendations to improve automation performance, service delivery, and capacity forecasting.
Develop reusable components, templates, and frameworks that support analytics and automation scalability across DWX.
Collaborate with other data scientists, analysts, and developers to implement best practices in model development and lifecycle management.
We are all different, yet we all use our outstanding contributions to serve patients. The vital attribute professional we seek is with these qualifications.
Data Science & ML: Proficient in Python (preferred) or R; experience with libraries such as scikit-learn, pandas, NumPy, XGBoost, and familiarity with TensorFlow or PyTorch. Skilled in supervised/unsupervised learning, time series forecasting, feature engineering, and hyper parameter tuning.
SQL & Data Engineering: Advanced SQL (joins, CTEs, window functions, optimization) with experience in relational databases (PostgreSQL, SQL Server, Oracle, MySQL) and data modeling (star/snowflake schema).
Databricks: Hands-on experience with Databricks notebooks (Python, SQL, PySpark), Delta Lake, MLflow, Unity Catalog, and Lakehouse architecture.
Visualization: Power BI expertise (DAX, Power Query, data model optimization) and/or Tableau (advanced calculations, LODs, performance tuning).
MLOps & Automation: Experience with Airflow, SageMaker, or Azure ML; knowledge of CI/CD for ML models and automated data pipelines.
Generative AI: Understanding of foundation models (LLMs, transformers, diffusion); hands-on experience with APIs from OpenAI, Azure OpenAI, HuggingFace; prompt engineering for summarization, classification, Q&A, and content generation; bonus for fine-tuning, RAG pipelines, or vector databases (FAISS, Pinecone).
Cloud Platforms: Familiarity with Azure (Data Factory, Synapse, Azure ML, Azure OpenAI), AWS (S3, Redshift, SageMaker), or GCP (BigQuery, Vertex AI).
ITSM & Agile: Exposure to ITIL practices or ITSM platforms (e.g., ServiceNow) and working knowledge of Agile/SAFe environments
