- 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

Work Hours :

- 06 : 00PM To 02 : 00 AM IST