Data Science Lead

Guide advanced analytics squads across exploration, model design, and evaluation to ship production-grade intelligence.

• Own EDA frameworks, hypothesis design, and feature engineering guidelines

• Curate high-quality datasets and evaluate data readiness for modeling

• Select algorithms aligned to business latency, interpretability, and cost targets

• Review experiments, statistical rigor, and validation artifacts

• Coach data scientists on storytelling, reproducibility, and peer reviews

• 8+ years in applied data science with shipped models in production

• Strong Python, PySpark, TensorFlow/PyTorch, and MLflow experience

• Depth in classical ML, deep learning, and evaluation metrics

• Hands-on with experimentation stacks, A/B testing, and causal inference

• Proven leadership mentoring senior ICs and cross-domain pods

• Python

• PySpark

• TensorFlow

• PyTorch

• MLflow

• Machine Learning

• Deep Learning

• A/B Testing

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