Senior Data Scientist with Prophet, Python

EPAM Systems; EE. UU.; A distancia; USD 120.000 - 190.000; Jornada completa

Descripción de la vacante

EPAM Systems in the United States is seeking a Senior Data Scientist to build data-driven should-cost models using time-series and econometric methods for procurement analytics. You will analyze macroeconomic factors, model relationships between prices and spend data, and ensure explainability of outputs for stakeholders.

You will apply time-series validation and guard against leakage and overfitting. English proficiency and deep experience in data science are expected to drive actionable

Formación

• 3+ years of experience in data science focused on time-series and econometric modeling.
• Proficiency in Python, Prophet and Chronos for forecasting.
• Experience with procurement analytics, spend data and commodity indexing.
• Knowledge of economic indicators, spend analysis and supplier metrics.

Responsabilidades

• Develop 'should-cost' models using time-series and econometric methods.
• Incorporate macroeconomic factors and indicators as drivers of cost.
• Model relationships between commodity prices, spend data, and external variables.
• Make model outputs explainable to stakeholders, detailing driving factors.
• Apply time-series cross-validation to ensure robustness.
• Prevent data leakage and overfitting in time-series models.
• Differentiate correlation from cointegration and apply appropriately.
• Select relevant features for time-series and econometric models.

Conocimientos

Time-series modeling

Econometric modeling

Explainable AI

Procurement analytics

Herramientas

Python

Prophet

Chronos

Descripción del empleo

We are seeking a Senior Data Scientist to build data-driven "should-cost" models that estimate what goods and services ought to cost, leveraging time-series and econometric methods to support procurement analytics.

Responsibilities

• Develop "should-cost" models using time-series and econometric methods
• Incorporate macroeconomic factors and economic indicators as drivers of cost
• Model the relationships between commodity prices, spend data, and external economic variables
• Make model outputs explainable to stakeholders, showing which factors drive each cost estimate
• Apply time-series cross-validation techniques to ensure model robustness
• Prevent data leakage and overfitting in time-series models
• Distinguish between correlation and cointegration, and determine when each applies
• Select relevant features for time-series and econometric models
• Work with spend data and commodity price indexing to support procurement analytics

Requirements

• 3+ years of experience in data science, with a focus on time-series and econometric modeling
• Proficiency in Python, Prophet, and Chronos
• Experience in procurement analytics, working with spend data and commodity price indexing
• Understanding of economic indicators, spend analysis, commodity pricing, and supplier diversity metrics
• Expertise in time-series cross-validation and explainable AI
• Proven ability to prevent data leakage and overfitting in time-series models
• Understanding of the difference between correlation and cointegration, and when each applies
• English proficiency at B2 level or higher