Data Scientist - TIme Series (Philadelphia)

Senior Data Scientist – Econometrics & Time SeriesLocation: Philadelphia, PA (Hybrid – 2–3 Days Onsite)Type: Full-TimeRole OverviewWe 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 ResponsibilitiesBuild and deploy advanced time series forecasting models including ARIMA, SARIMA, VAR, and state-space modelsApply econometric techniques such as WLS, regression diagnostics, panel data models, and causal inference methodsDevelop Bayesian and probabilistic models for uncertainty estimation and decision-makingUtilize Markov chains and stochastic modeling techniques for behavioral and sequential data analysisTranslate complex business problems into scalable analytical solutions and actionable insightsWork with large-scale datasets using Databricks and modern analytics platformsPartner with business and technical stakeholders to drive data-driven decision makingMentor junior data scientists and promote best practices in statistical modeling and experimentationRequired SkillsStrong expertise in Econometrics and Time Series AnalysisHands-on experience with:ARIMA, SARIMA, VAR, forecasting modelsRegression diagnostics, WLS, panel data modelsCausal inference and experimentation frameworksBayesian statistics and probabilistic modelingMarkov chains and stochastic processesStrong programming skills in Python and SQLExperience with Databricks or similar big data environmentsExcellent communication and stakeholder management skillsNice to HaveExperience with machine learning models and predictive analyticsKnowledge of feature engineering, model validation, and performance tuningExposure to ML pipelines and MLOps conceptsTelecommunications domain experience is a plus