Data Scientist

Key Responsibilities

• Architect and scale manufacturing data ecosystems by designing and implementing robust methods, processes, and systems to ingest, consolidate, and analyze structured and unstructured data from diverse plant, supply chain, and engineering sources.

• Lead advanced analytics and modeling initiatives by applying statistical, machine learning, and data mining techniques (Python, R) to solve complex manufacturing problems such as yield optimization, predictive maintenance, and constraint resolution.

• Drive agentic workflows and intelligent automation by building autonomous, AI-powered pipelines that orchestrate data ingestion, feature engineering, model execution, and decision-making at scale, reducing manual intervention and accelerating time-to-insight.

• Define and optimize data architecture including data acquisition strategies, semantic layers, and scalable data models that support real-time analytics, digital twins, and AI-driven manufacturing use cases.

• Translate data into actionable business outcomes by partnering closely with manufacturing operations, supply chain, and product engineering teams to define KPIs, uncover insights, and operationalize recommendations.

• Develop production-grade analytics solutions by designing algorithms, models, and automation pipelines leveraging SQL, Python, and modern data platforms to cleanse, integrate, and process large-scale industrial datasets.

• Enable experimentation and continuous improvement by collaborating with product, engineering, and operations teams to frame hypotheses, design experiments, and uncover deeper correlations that extend beyond current measurement systems.

• Communicate insights with executive impact by translating complex analytical findings into clear, compelling narratives and visualizations that influence senior leadership decision-making and operational strategies.

Functional Expertise

• Recognized as a thought leader in manufacturing data science, with deep expertise in advanced analytics, AI/ML, and industrial data systems, complemented by strong cross-domain knowledge (supply chain, quality, engineering).

• 10+ years of experience in agent-based systems, AI orchestration frameworks, and workflow automation, enabling scalable and reusable analytics solutions.

Business Acumen

• Proactively anticipates manufacturing, supply chain, and regulatory challenges, recommending data-driven improvements to processes, product quality, and operational efficiency.

• Aligns analytics initiatives with strategic business priorities, driving measurable impact across cost, throughput, yield, and cycle time.

Problem Solving

• Tackles highly complex, ambiguous problems with significant business impact using innovative analytical approaches, including AI-driven simulations, optimization models, and graph-based reasoning.

• Designs end-to-end intelligent systems that integrate data, models, and decision logic into automated workflows.

Impact

• Influences strategic direction, investment decisions, and resource allocation for analytics and AI programs within manufacturing.

• Establishes best practices for data products, automation frameworks, and AI adoption across global operations.

Interpersonal Leadership

• Effectively communicates complex technical concepts to senior stakeholders, anticipating objections and driving alignment across cross-functional teams.

• Leads and mentors multi-disciplinary teams (data science, engineering, analytics, and UI/API) to deliver high-impact, production-ready solutions.