Data Scientist

What Youll 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.

• Identifyfurther 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 trainvalidate andoptimizemachine learning models for predictive and prescriptive analytics.

• Build andmaintaindata 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 withappropriate statisticalmetrics 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 Youll 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 andmonitor forconcept 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 governancevalidatingdata sources before model development.

Continuous Learning & Collaboration

• Stay current with advances in data science AI and machine learning and bringnew ideasto the team.

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

What Youll Bring:

• 25 years of experience as a Data Scientist Machine Learning Engineer or in a similar analytical role.

• Hands-on experience with Databricks andPySparkfor 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 LearningMLflow or similarMLOpsplatforms.

• 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).

• BachelorsorMastersdegree in a quantitative field (Computer Science Statistics Mathematics AI or related).