People Data Scientist

- Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and SQL. - Experience working with HR data sources (Workday, SuccessFactors, Oracle HCM, LinkedIn Talent Insights, etc.) or related workforce datasets. - Knowledge of people analytics methodologies such as attrition modeling, pay equity analysis, employee lifetime value, skills inference, or organizational network analysis. - Familiarity with big data frameworks (Spark, Databricks, Dask) and cloud platforms (AWS, Azure, GCP). - Knowledge of Snowflake and experience integrating with HR and business data. - Familiarity with MLOps principles, CI/CD, and deploying ML and AI models in production environments, including monitoring and retraining pipelines. - Strong understanding of machine learning algorithms for classification, regression, clustering, and time series forecasting, plus exposure to advanced AI techniques such as natural language processing (NLP), large language models (LLMs), and generative AI. - Experience with data visualization tools (Tableau, Power BI, or Python-based libraries). - Excellent problem-solving skills and ability to translate complex technical analyses into clear, actionable insights for non-technical audiences. - Familiarity with vector databases, embedding-based retrieval, and prompt engineering to support AI-enabled HR solutions. - Understanding of ethical AI principles, bias detection, and responsible AI practices in HR contexts.