Data Scientist - TIme Series
Acunor Inc
Philadelphia, United States of America
3 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Philadelphia, United States of America
Tech stack
Data analysis
Big Data
Computer Programming
Python
Machine Learning
Performance Tuning
Standard Sql
Feature Engineering
Model Validation
Build Management
Machine Learning Operations
Markov
Databricks
Job description
- 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
Requirements
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., * 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