A Fortune 50 client is looking for a Data Scientist to support their IBP program. They are doing demand forecasting which is used by the development team and connecting with demand planners on how to make the forecast better, and use the engine created by the core team to run the forecast and integrate all the data into one snapshot. IBP has data around a ton of forecasting, but this team does the development of models. Their goal is to help boost the demands and make the forecast aligned.
Key Responsibilities
Time Series Modeling & Forecasting
Monitor model performance, retrain models as needed, and implement improvements to reduce forecast error.
Build, tune, and evaluate supervised learning models (e.g., XGBoost, Random Forest) for classification and regression tasks.
Perform feature engineering, selection, and validation to optimize model accuracy and robustness.
Pipeline Orchestration
Design and implement ETL workflows in Azure Data Factory (or similar) to ingest, cleanse, and transform large datasets.
Leverage Databricks and Spark to process and analyze big data efficiently.
Insights & Reporting
Create interactive dashboards and visualizations in Power BI to communicate findings to stakeholders.
Translate complex analytical outcomes into clear, actionable business recommendations.
Collaboration & Documentation
Work closely with product owners and business stakeholders to define requirements and success metrics.
Document methodologies, code, and data pipelines following best practices and version control (Git).
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5+ years as a Data Scientist, Data Analyst, or similar role in an analytics environment
Proven track record building time series and machine learning models in production
Programming: Python (pandas, NumPy, scikit learn), SQL (T SQL, PostgreSQL).
Big Data & Cloud: Databricks, Apache Spark.
Knowledge with different models such as: ARIMA/SARIMA, Prophet, Crostons method, ETS, XGBoost, Random Forest, and/or Gradient Boosting.
ETL & Orchestration: Azure Data Factory or equivalent (e.g., AWS Glue, Google Cloud Dataflow).
Model Management: MLflow or similar for experiment tracking and model registry.
Deployment: Docker, CI/CD tools (e.g., Azure DevOps, Jenkins).
Visualization: Power BI, Tableau, or equivalent
Fluent in English and Spanish
Ability to work in fast paced global enterprise environment
Excellent team player
Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.