Job Responsibilities:
- Hands-on developer in building models in complex data environments with unambiguous requirements and enjoys data.
- Advance causal inference techniques to improve outage predictions measurement, including uplift modeling, propensity scoring, and Bayesian methods.
- Develop forecasting models to predict outages performance, resource allocation impacts, and long-term customer impacts for operations teams.
- Enhance ad testing methodologies, including A/B testing, synthetic control experiments, and incrementally testing, to measure efficiency accurately.
- Optimize operational team decisioning, using reinforcement learning, Bayesian optimization, and econometric modeling.
- Work closely with data engineers to scale and automate model deployment, integrating ML solutions into production environments.
Required Qualifications:
- Bachelor's Degree Quantitative Field (math, statistics, economics, computer science, physics, engineering, etc.) (Required).
- Master's/Advanced Degree Quantitative Field (math, statistics, economics, computer science, physics, engineering) (Preferred).
- Proven experience in Fault Prediction models, with a focus on attribution modeling, causal inference, and model mix optimization.
- Strong background in machine learning, fault metrics, and statistical modeling RAN applications.
- Expertise in Python, SQL, and cloud platforms (Azure, Snowflake, Databricks) for scalable modeling and data processing.
- Experience with experimental design and incrementality testing in telecom RAN networking subject areas.
- Ability to communicate complex data science concepts to both technical and non-technical stakeholders, influencing business decisions
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