Data Scientist

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Client is seeking a hands-on Applied ML / Application Engineer to support a global manufacturing organization focused on process chemical manufacturing. This is a critical role that sits at the intersection of data science, machine learning, and manufacturing operations. The ideal candidate understands how to apply data science principles in a production environment and can work closely with both business and operational teams to drive value through anomaly detection, yield optimization, and process improvements.

Note: Preference is local Curitiba Brazil for sporadic onsite visits.

• Collaborate with business stakeholders, manufacturing engineers, and data teams to translate operational challenges into ML-driven solutions.
• Utilize existing ML models (and adjust where necessary) to support yield improvement, root-cause analysis, and anomaly detection.
• Understand contextual asset and process data to build digital threads across manufacturing workflows.
• Act as a bridge between the manufacturing floor and data/ML teams—communicating technical concepts in a way that's digestible to non-technical stakeholders.
• Work with data from multiple systems including MES, QMS, LIMS, process historians, and time series platforms.
• Learn and leverage TwinThread platform to operationalize and deploy ML models.
• Collaborate with engineering teams on data ingestion and pipeline development when necessary.
• Support visualization efforts that convey model insights and drive data-driven decision making.
• Ensure models align with existing ISA-95 / ISA-88 architectures across batch and continuous processing environments.

• 3–7 years’ experience in applied ML / data science / data engineering roles, preferably in a process manufacturing or chemical manufacturing setting.
• Background in chemical engineering, industrial automation, or manufacturing systems.
• Knowledge of ISA-95 / ISA-88 standards
• Strong understanding of manufacturing operations, particularly continuous and batch processing systems.
• Proven experience with anomaly detection, predictive maintenance, or yield optimization in an industrial context.
• Familiarity with time-series data, process historians, QMS, LIMS, MES, and related systems.
• Comfortable interacting with both business stakeholders and hands-on operational staff; strong communication and stakeholder engagement skills.
• Experience working with TwinThread or similar industrial AI platforms a strong plus (willingness to learn is acceptable).
• Experience with data visualization tools and storytelling with data.
• Familiarity with cloud platforms and common ML libraries (e.g., scikit-learn, PyTorch, TensorFlow) is beneficial but not essential.

• Experience with model deployment in production systems.
• Exposure to digital twin or digital thread concepts.
• English and Portuguese speaking (they have translators if not).

• Mid-Senior level

• Contract

• Information Technology

• Information Services