Data Scientist - TIme Series

Senior Data Scientist – Econometrics & Time Series

Location: Philadelphia, PA (Hybrid – 2–3 Days Onsite)

Type: Full-Time

Role Overview

We are seeking a Senior Data Scientist with strong expertise in Econometrics and Time Series Analysis to support advanced analytics initiatives for a large Telecommunications environment. This role focuses on forecasting, causal inference, customer behavior analytics, and statistical modeling using large-scale datasets.

The ideal candidate will have deep hands-on experience with econometric techniques, probabilistic modeling, and time series forecasting frameworks, along with strong Python and SQL skills.

Key Responsibilities

• Build and deploy advanced time series forecasting models including ARIMA, SARIMA, VAR, and state-space models
• Apply econometric techniques such as WLS, regression diagnostics, panel data models, and causal inference methods
• Develop Bayesian and probabilistic models for uncertainty estimation and decision-making
• Utilize Markov chains and stochastic modeling techniques for behavioral and sequential data analysis
• Translate complex business problems into scalable analytical solutions and actionable insights
• Work with large-scale datasets using Databricks and modern analytics platforms
• Partner with business and technical stakeholders to drive data-driven decision making
• Mentor junior data scientists and promote best practices in statistical modeling and experimentation

Required Skills

• Strong expertise in Econometrics and Time Series Analysis
• Hands-on experience with:
• ARIMA, SARIMA, VAR, forecasting models
• Regression diagnostics, WLS, panel data models
• Causal inference and experimentation frameworks
• Bayesian statistics and probabilistic modeling
• Markov chains and stochastic processes
• Strong programming skills in Python and SQL
• Experience with Databricks or similar big data environments
• Excellent communication and stakeholder management skills

Nice to Have

• Experience with machine learning models and predictive analytics
• Knowledge of feature engineering, model validation, and performance tuning
• Exposure to ML pipelines and MLOps concepts
• Telecommunications domain experience is a plus