Senior Data Analytics Engineer

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape.

Sandisk meets people and businesses at the intersection of their aspirations and the moment, enabling them to keep moving and pushing possibility forward. We do this through the balance of our powerhouse manufacturing capabilities and our industry-leading portfolio of products that are recognized globally for innovation, performance and quality.

Sandisk has two facilities recognized by the World Economic Forum as part of the Global Lighthouse Network for advanced 4IR innovations. These facilities were also recognized as Sustainability Lighthouses for breakthroughs in efficient operations. With our global reach, we ensure the global supply chain has access to the Flash memory it needs to keep our world moving forward.

Responsibilities

Identify, analyse, and resolve manufacturing process and quality-related issues through data-driven approaches.; Develop, implement, and maintain Fault Detection (FD), Predictive Analytics, and Run-to-Run (R2R) control models to improve process stability, yield, and equipment performance.; Lead and participate in continuous improvement initiatives focused on yield enhancement, cycle time reduction, productivity improvement, and manufacturing cost optimisation.; Perform exploratory data analysis (EDA) across multiple manufacturing data sources to uncover patterns, root causes, and improvement opportunities.; Design, develop, and deploy Advanced Process Control (APC) and Machine Learning models to address manufacturing quality and process challenges.; Collaborate with cross-functional teams including Process Engineering, Equipment Engineering, Quality, IT, and Operations to implement and sustain SPC, FDC, RMS, APC, and ML-based solutions .; Validate data quality, ensure data completeness, and monitor model performance to maintain solution effectiveness and reliability.; Develop model monitoring and retraining strategies to ensure long-term predictive accuracy and business value.; Document analytical methodologies, model assumptions, results, and lessons learned in knowledge management systems, ensuring documentation remains current and accessible.; Extract, transform, and analyse data using SQL, NoSQL, Python, Spark, and other analytics tools from manufacturing databases, historians, MES, equipment logs, and cloud platforms.; Communicate technical findings and recommendations effectively to stakeholders through dashboards, reports, and presentations.; Stay current with emerging technologies in Data Analytics, Artificial Intelligence (AI), Machine Learning (ML), Industrial IoT, and Smart Manufacturing to drive innovation and operational excellence.

Requirements

At least 3 years of working experience in related field; Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or related disciplines.; Familiar with semiconductor manufacturing environment is an added advantage.; Strong problem-solving skills with the ability to translate complex manufacturing challenges into analytical and ML solutions.; Effective communication and stakeholder management skills with the ability to work across global and cross-functional teams.; Strong experience in Python, SQL, data analytics, and machine learning .; Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar .; Basic knowledge of SQL; Good English skill, both written and oral; Good team work spirit; Able to develop and implement projects independently

Nice to have

Knowledge of manufacturing systems, including SPC, FDC, APC, RMS, MES, and semiconductor manufacturing processes is an advantage.; Familiarity with cloud-based analytics platforms, data pipelines, and big data technologies.