RMO Sputter Process Engineering Intern, Data Science (Singapore, SG)

<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>About our group:</b></H2> </div><div><p><span style="font-size:11.0pt"><span><span style="font-family:&#39;Calibri&#39;, sans-serif"><p>The RMO Sputter Process Engineering team is responsible for driving process control, monitoring, and continuous improvement for PMR and HAMR media products. The team supports stable, high‑quality manufacturing by optimizing ultra‑high vacuum (UHV) sputter systems, improving overall equipment effectiveness (OEE), reducing downtime, and enhancing cost efficiency across hardware and consumables. In addition, the group plays a key role in new product readiness, process conversions, media performance improvement, and defect reduction through data‑driven engineering solutions and close collaboration with cross‑functional partners.</p></span></span></span></p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>About the role - you will:</b></H2> </div><div><p><span style="font-size:11.0pt"><span><span style="font-family:&#39;Calibri&#39;, sans-serif"><p>As a <strong>RMO Sputter Process Engineering Intern</strong>, you will support digital transformation and smart manufacturing initiatives within sputter operations by applying data science and analytics techniques to real production environments.You will:</p> <ul type="disc"> <li>Support the development of smart sputter systems by assisting in building data‑driven models for process control, prediction, and quality monitoring</li> <li>Analyze large engineering datasets to identify trends, correlations, and potential root causes related to process performance, yield, and defects</li> <li>Support the refinement of Key Process Input Variable (KPIV) anomaly detection systems for HAMR and PMR processes</li> <li>Develop and maintain dashboards (e.g., Tableau) to visualize process performance, equipment health, and OEE insights</li> <li>Assist in prototyping, testing, and evaluating machine learning and AI models for manufacturing applications</li> <li>Collaborate with cross‑functional teams including Process, Equipment, Maintenance, Launch, and IT to translate data insights into actionable improvements</li> </ul> <p><strong>What Will You Learn and Embark on at the Start?</strong></p> <p>During this internship, you will gain hands‑on experience working with real production data (HAMR/PMR) and exposure to semiconductor and thin‑film manufacturing processes. You will develop practical experience applying AI and machine learning in a manufacturing environment, learn process control and OEE analytics, and contribute to solutions that can directly impact production performance.</p></span></span></span></p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>About you:</b></H2> </div><div><p><span style="font-size:11.0pt"><span><span style="font-family:&#39;Calibri&#39;, sans-serif"><p>We’re looking for someone who is:</p> <ul type="disc"> <li>Curious, proactive, and eager to learn in a high‑tech manufacturing environment</li> <li>Analytical and detail‑oriented, with a strong interest in problem‑solving</li> <li>Comfortable managing multiple tasks in a fast‑paced setting</li> <li>A collaborative team player with clear communication skills</li> <li>Self‑driven and open to exploring new tools, ideas, and approaches</li> <li>Able to commit to a <strong>full‑time internship</strong></li> </ul></span></span></span></p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Your experience includes:</b></H2> </div><div><p><span style="font-size:11.0pt"><span><span style="font-family:&#39;Calibri&#39;, sans-serif"><ul type="disc"> <li>Currently pursuing a Bachelor’s degree in Data Science, Computer Science, Engineering, AI, Analytics, or a related discipline</li> <li>Programming experience in Python, R, C++, or C#</li> <li>Exposure to machine learning model development or deployment</li> <li>Experience with data visualization tools such as Tableau or Power BI</li> <li>Data analytics and basic statistical concepts</li> <li>SQL or database querying</li> <li>Foundational understanding of machine learning or AI concepts</li> <li>Familiarity with GitHub</li> <li>Exposure to Linux environments is a plus</li> <li>Basic web technologies (JavaScript, HTML, JSON)</li> </ul></span></span></span></p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="font-size:16.0px;word-wrap:break-word"><H2 style="font-size:1.0em;margin:0.0px"><b>Location:</b></H2> </div><div><p><div id="tor__fcustJPLoc_readOnlyDiv"> <div id="tor__fcustJPLoc_content"> <p>Our Woodlands site is one of the largest electronics manufacturing sites in Singapore, housing our recording media operations. Spread over three sites, it is easily accessible via bus or from the MRT Station, with many employees taking mass transportation to work. Here at work, you can enjoy breakfast, lunch, dinner, and snacks at our onsite canteen and coffee shop. We offer a range of facilities including an in-house gym and dance studio, as well as after-work badminton and table tennis competitions. On-site celebrations and community volunteer opportunities also abound.</p> </div> </div></p> <p> </p> <p><b>Location</b>: Woodlands, Singapore, W1<br> <b>Travel</b>: None</p></div></div></div>