Job description

Job Responsibilities:

Build and maintain univariate time series feature analysis
Test and integrate new univariate time series models into IBP process
Participate in the development and deployment of complex multivariate forecasting models
Interface with IBP process representatives on model trouble shooting and new feature analysis
Interface with analytical and subject matter experts throughout SHS organization in the pursuit of improved forecasting outcomes
Provide expert consultation for focused reviews with management team when needed.
Maintain documentation of and control framework for deployed models and tools

Skill Set:

7 to 10 years of experience
3-5 years of experience with classic time series analysis tools (e.g. ARIMA, State Space Models, VAR, Error Correction Models etc.)
3-5 years of experience with machine learning models including Decision Tree, Regression, Gradient Boosting, and Deep Learning
3-5 years of experience of Python / R coding
3-5 years of experience with SQL coding
Experience in working on Azure tech stack
Experience building and deploying production time series forecast models
Experience managing code repositories.
Experience in Decision Science area / Operations Research / Applied Mathematics, Statistics, Data Science or Computer Science.
Knowledge on LLMs

Nice to have:

6+ years of experience implementing and managing time series analysis and forecast models
6+ years of experience implementing and managing machine learning models
Experience working on projects in Supply Chain Management or Marketing analysis.
Experience working with high volume time series forecasting
Knowledge of In Vitro Diagnostics market space
Experience building & deploying Structural Equation Models
Experience utilizing MLFlow
Experience working in SnowFlake
Familiarity with Azure Databricks
Familiarity with M Competition outcomes
Experience in GenAI projects
Masters in Economics, Operations Research, Applied Mathematics, Statistics, or Data Science

Data Scientist