Business Data Scientist – Advanced Analytics

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What we offer:

Group Summary:

We are looking for a Data Scientist who can work closely with business stakeholders to design, build, and operationalize advanced forecasting and analytics solutions across various business functions like procurement, sales, finance, treasury, or quality. This role combines classical machine learning with emerging GenAI/LLM techniques to deliver high-impact insights for senior leadership. You will manage models end-to-end, converting strategic business questions into scalable, production-ready solutions that support decision making and early-warning capabilities.

Key Responsibilities

• Collaborate with business teams to translate domain problems (e.g., spend, sales, cash flow, quality trends) into analytical and forecasting use cases.

• Own model development from concept to deployment, including data preparation, feature engineering, model design, testing, and monitoring on Databricks.

• Build time-series, regression, anomaly detection, and early-warning models for leadership reporting and high-impact business decisions.

• Identify and apply GenAI/LLM approaches—such as narrative generation, scenario simulation, or hybrid modeling—where they bring clear value.

• Work closely with Data Engineering to define data requirements and ensure high-quality, reliable pipelines.

• Communicate analytical outcomes and recommendations in clear business terms for executive stakeholders.

• Apply MLOps practices (MLflow, Model Registry, workflows) for reproducibility and operational robustness.

• Support the maturation of the organization’s AI/ML landscape by contributing patterns, best practices, and model lifecycle improvements.

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Economics, or related field.

• 4–6 years of experience in data science with a track record of delivering end-to-end forecasting or business-focused ML solutions.

• Strong proficiency in Python and SQL; experience with Databricks for analytics, ML, and data pipelines.

• Solid grounding in time-series and classical ML methods, with experience deploying models in production environments.

• Strong communication and stakeholder engagement skills, with the ability to collaborate with non-technical business leaders.

• Experience applying MLOps practices (reproducibility, CI/CD, monitoring, lifecycle management).

Preferred Skills

• Experience working with business functions like sales, finance, procurement, treasury, or quality analytics in a manufacturing or enterprise context.

• Familiarity with GenAI/LLM techniques and practical application to business workflows.

• Exposure to product-centric work with cross-functional teams.

• Knowledge of visualization tools such as Power BI or Tableau.

• Understanding of manufacturing, automotive, or large enterprise business processes.

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