Data Scientist

Aquent is partnering with a leading global innovator at the forefront of transforming manufacturing operations. This organization is dedicated to revolutionizing how manufacturing data is utilized, directly impacting production lines, product quality, and operational efficiency across numerous facilities. Join a team where your work will solve complex challenges and shape the future of intelligent manufacturing, seeing your ideas come to life and driving significant change.

Step into an unparalleled opportunity to be instrumental in developing a brand-new application and system. This is a greenfield project, offering you the creative freedom to design and implement innovative AI solutions from the ground up. As a key contributor, you will leverage a wealth of manufacturing and quality data to build sophisticated data models and AI applications that profoundly enhance safety, quality, throughput, cost-efficiency, and equipment reliability.

If you thrive on digging into complex data, uncovering insights, and building solutions with a tangible, enterprise-wide impact, this role is for you.

What You’ll Do

• Translate complex operational and business challenges into measurable analytical questions and actionable use cases.
• Identify, access, and critically assess data from diverse manufacturing sources, including operational execution systems, quality systems, equipment historians, and maintenance platforms.
• Design and build robust analytical datasets and scalable data pipelines using industry-leading tools such as SQL, Python, Spark, and Databricks.
• Conduct in-depth exploratory analysis, statistical studies, root-cause investigations, forecasting, optimization, and experimentation to drive data-driven decisions.
• Develop, rigorously validate, document, and continuously monitor predictive or prescriptive models for critical use cases like downtime prediction, scrap reduction, defect detection, bottleneck identification, anomaly detection, yield optimization, and preventive maintenance.
• Evaluate data quality, lineage, coverage, potential missingness, bias, and overall operational readiness before model deployment.
• Transform complex analytical findings into clear, practical recommendations that empower plant personnel and leaders in their daily decision-making.
• Create intuitive dashboards, compelling visualizations, comprehensive reports, and user interfaces that effectively communicate trends, risks, and opportunities.
• Collaborate with data engineering, application development, and operational technology teams to successfully product ionize analytics and models.
• Monitor model performance, detect data drift, ensure pipeline health, and track business impact post-deployment.
• Support strategic modernization initiatives, including cloud migration, data-product development, automation, and the retirement of legacy systems.
• Adhere to strict data governance, cybersecurity, AI governance, safety, privacy, and change-management requirements.
• Present complex technical results to both technical and non-technical audiences, maintaining clear and thorough documentation.
• Champion reusable analytical methods, standards, and best practices across various manufacturing domains.

What You’ll Bring

Must-Have Qualifications:

• A Bachelor’s degree in a technical field such as Computer Science, Computer Engineering, or a related discipline.
• At least 8-10 years of professional experience, with a significant focus on data.
• Extensive experience (8-10 years) in data modeling, designing and implementing effective data structures.
• Proven expertise (8-10 years) in building and managing robust data pipelines.
• Demonstrated ability (8-10 years) in data analytics, specifically extracting meaningful insights and building solutions from complex and often messy data.
• Proficiency in programming languages and tools such as SQL, Python, Spark, and Databricks.
• Strong background in applying statistics, machine learning, and optimization techniques.
• Excellent communication skills, capable of presenting technical information to diverse audiences and fostering collaboration across teams.
• Legal eligibility to work in the United States without employer sponsorship (direct…