What You'll Do :

• Initial focus will be on automating forecasting and scenario simulation processes for Sales, Finance, and Commercial Support; future scope may extend to other CBS functions. 

• Identify further business problems across Purchasing, Finance, Logistics, Sales Administration, and Personnel that can be solved through statistics, machine learning, or AI. 

• Collect, clean, and prepare large structured and unstructured datasets; run exploratory data analysis to uncover patterns, trends, and business opportunities. 

• Develop, train, validate, and optimize machine learning models for predictive and prescriptive analytics. 

• Build and maintain data science solutions using Databricks and Azure ML Studio, collaborating with Data Engineers to prepare data, develop features, and industrialize analytical and machine learning use cases. 

• Deploy and monitor machine learning models in production, evaluating performance with appropriate statistical metrics and retraining as needed. 

• Communicate analytical findings and forecast outputs to technical and non-technical stakeholders, including Sales and Finance leadership, through clear visualizations and presentations. 

• Support AI-driven process automation by integrating predictive models into business workflows, working cross-functionally to keep solutions aligned with business goals. 

• Document models, assumptions, methodologies, and analytical processes according to company standards. 

How You'll Work :

Stakeholder Communication 

• Translate complex business data into actionable insights and forecast recommendations for Sales, Finance, and commercial support teams. 

• Explain model and forecast performance, assumptions, and limitations clearly to both technical and non-technical audiences. 

Model Governance & Data Quality 

• Build reliable, interpretable models, and monitor for concept drift or performance degradation as forecast cadence increases. 

• Ensure reproducibility through version control, documentation, and experiment tracking. 

• Apply company standards for data quality, security, privacy, and governance, validating data sources before model development. 

Continuous Learning & Collaboration 

• Stay current with advances in data science, AI, and machine learning, and bring new ideas to the team. 

• Share knowledge with colleagues through documentation, code reviews, and mentoring. 

What You'll Bring:

• 2–5 years of experience as a Data Scientist, Machine Learning Engineer, or in a similar analytical role. 

• Hands-on experience with Databricks and PySpark for scalable data processing, and strong Python programming skills (including Pandas, NumPy, and scikit-learn) for developing, training, and evaluating machine learning models. 

• Good knowledge of machine learning techniques, including regression, classification, clustering, recommendation systems, time-series forecasting, and anomaly detection. 

• Solid understanding of statistics, probability, hypothesis testing, and experimental design. 

• Experience with data preprocessing, feature engineering, feature selection, model evaluation, hyperparameter tuning, and cross-validation. 

• Strong SQL skills for querying, transforming, and analyzing large datasets. 

Nice to Have 

• Experience with Azure Machine Learning, MLflow, or similar MLOps platforms. 

• Exposure to LLMs, GenAI, or AI agent workflows. 

• Power BI or similar for communicating insights to stakeholders. 

• Background in FP&A, demand planning, or a Global Business Services / Shared Services environment. 

• Experience with ERP or financial consolidation systems (e.g., SAP, Oracle). 

• Bachelor’s or Master's degree in a quantitative field (Computer Science, Statistics, Mathematics, AI, or related).