Responsibilities
- Develop and deploy statistical models and machine learning algorithms using Python
- Perform exploratory data analysis (EDA) to identify trends, risks, and actionable insights
- Design and evaluate models for classification, regression, clustering, and anomaly detection
- Build and optimize ML models supporting Defensive AI, automation, and intelligence use cases
- Perform feature engineering and model tuning for performance, scalability, and explainability
- Prepare and validate datasets for GenAI, LLM, and advanced ML workflows
- Partner with data engineering and platform teams to operationalize models
- Support model monitoring, performance tracking, and continuous improvement
- Ensure models align with enterprise standards for security, governance, and compliance
- Collaborate closely with Data Engineering, AI Platform, Product, and Security teams
- Translate business problems into data science solutions and measurable outcomes
- Communicate insights and model results clearly to technical and non‑technical stakeholders
Requirements
- Experience working with large, complex, enterprise‑scale datasets
- GenAI / LLM data preparation and understanding of prompt‑ready datasets
- Proven solid Python for data analysis, ML modeling, and automation
- Proven applied statistics & machine learning (classification, regression, anomaly detection)
- Proven feature engineering & model evaluation techniques
- Proven SQL for data analysis and large‑scale dataset exploration
Nice to Have
- Experience working in Agile, cross‑functional product teams
- Knowledge of cloud platforms (Azure preferred)
- Familiarity with MLOps, model monitoring, and CI/CD for ML
- Proven exposure to Databricks / Spark / Big Data ecosystems
