Data Scientist

Senior Data Scientist

The Senior Data Scientist will collaborate with stakeholders, other data scientists, data engineers, and analysts to develop sophisticated measurement frameworks, improve prediction models. The ideal candidate will have deep expertise in advanced models’ development in complex networking data sets, experimental design, ensuring that operational team strategies are backed by robust, data-driven insights.

Work Hours

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