We are looking for a Senior Data & Automation Engineer to join our Product Analytics team and drive data-driven solutions, automation, and analytics tools across our manufacturing operations. If you're passionate about building impactful systems, have strong software engineering skills combined with data analytics expertise, and thrive in a collaborative environment, we'd love to hear from you!
What you'll be doing:
Design, develop, and deploy automation solutions, dashboards, and analytical tools using Python Build and maintain data pipelines and workflows to improve data integrity and streamline data flows across systems
Participate as a data analyst and technical contributor in cross-functional task forces addressing critical manufacturing and quality challenges
Perform system characterizations and collaborate with internal teams to identify opportunities for optimization and deliver custom solutions
Develop machine learning and AI-driven solutions for anomaly detection, quality control, and predictive analytics
Present technical insights and recommendations to stakeholders and drive data-informed decision-making
What we need to see:
BSc in Computer Science, Engineering, Information Systems, or relevant field
5+ years of hands-on software development and data analysis experience
Strong proficiency in Python with experience in data science libraries (Pandas, NumPy, SciPy, Matplotlib)
Experience with machine learning frameworks (Scikit-learn, XGBoost) and AI/ML platforms
Proven track record of building automation tools, scripts, and production-ready software solutions
Experience with SQL and working with large-scale datasets Proficiency with version control systems (Git)
Experience in dashboard development and data visualization tools (Streamlit, Plotly, Dash, Power BI or similar)
Excellent communication and collaboration skills with ability to work across multiple teams Self-driven with a problem-solving mindset and ability to translate business needs into technical solutions
Ways to stand out from the crowd:
Background in manufacturing, yield engineering, or product engineering environments
Experience deploying ML models in production environments
Familiarity with CI/CD practices and automated testing