Senior Manager - Data Science

Role: Senior Manager Data Science

Location: Philadelphia, PA

Type: Hybrid (2-3 days / week in office)

Role Overview

We are looking for a Senior Manager – Data Science (Econometrics & Time Series) to lead advanced analytical initiatives for a major Telecommunications client.

This role is heavily focused on econometric modeling, time series analysis, and causal inference , with applications in forecasting, pricing, and customer behavior analytics. The ideal candidate brings deep expertise in statistical modeling and is comfortable working with large-scale data environments.

Key Responsibilities

• Lead development of time series forecasting models (ARIMA, VAR, state-space models, etc.) for business-critical use cases.
• Apply econometric techniques such as WLS, panel data models, and causal inference methods to solve real-world business problems.
• Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making.
• Utilize Markov chains and stochastic processes for modeling sequential or behavioral data.
• Translate business problems into robust analytical frameworks and deliver actionable insights.
• Work with large datasets using Databricks
• Collaborate with stakeholders across business and technical teams to ensure model relevance and impact.
• Mentor junior team members and drive best practices in statistical modeling and experimentation.

Must-Have Qualifications

• Strong foundation in econometrics and time series analysis (this is critical for the role).
• Hands-on experience with:
• Time series models (ARIMA, SARIMA, VAR, forecasting techniques)
• Econometric methods (WLS, regression diagnostics, panel data models)
• Causal inference (A/B testing, quasi-experimental methods)
• Bayesian statistics and probabilistic modeling
• Markov chains or stochastic modeling
• Proficiency in Python along with SQL .
• Experience working with Databricks or similar big data platforms .
• Ability to clearly communicate complex statistical concepts to non-technical stakeholders.

Secondary / Good-to-Have Skills (General Data Science)

• Experience with machine learning models (classification, regression, tree-based models, etc.)
• Familiarity with feature engineering, model validation, and performance tuning
• Exposure to ML pipelines and MLOps concepts