ResponsibilitiesLam Research is looking for a Performance Data Scientist in Lam's Engineering organization.
This is individual will partner with the MFE/TE/IE peers and leaders across the LMM MFE community to identify through the use a common data to effectively prioritize and resolve the Engineering issues in the company impacting our Manufacturing and production customers to meet both short and term business objectives.
This role will have a high-profile presence within the organization and will be able to contribute and make a difference in the Engineering performance and culture through knowledge, leadership and influence within the company and ultimately delivering required improvement to our valued customers and internal functions.
Essential functions/duties: Analyze trends and patterns in Engineering data that drive required Engineering improvement.
Strong data analytics skills, statistical knowledge and ability to leverage multiple data sources to enable data-driven insights and effective decision making.
Strong understanding of database structures and data integration, SQL programming skills, including the use of SQL analytics function, develop reports, dashboards , metric measurement and assessment methods for performance management and predictive modeling.
Improve data utilization via AI, automation and improve defect determination leading to real time resolution and speeding systemic action.
Ability to effectively interpret, communicate and articulate details into concise and understandable facts, numbers and actionable items.
Lead multiple projects simultaneously and demonstrate organizational, prioritization, and time management proficiencies.
Minimum Requirements Minimum of 10 years of proven continuous improvement analytical experience from a similar role, including project management and business analysis.
BS?degree?in engineering, quality?management, computer science, related/applicable science, or master's degree.
Semiconductor industry experience.
Exceptional knowledge and history of implementation of AI to solve the most complex challenges of the Lam that lead to efficiency and effectiveness improvements.
Ability to work on multiple problems simultaneously.
Knowledge of Quick base, Power BI capability and development.
Ability to present conclusions and recommendations to executive audiences.
Excellent Knowledge in SQL, Jmp, Python, R, Matlab, or equivalent.
Six Sigma certification (Green Belt or higher).
Advanced expertise in?structured problem-solving methodologies (PDCA, DMAIC,?8D)?and quality tools.
Experience in Statistical Process Control (SPC), Gage R&R, Pareto Charts etc.
using tools like JMP, Minitab.
Our Commitment We believe it is important for every person to feel valued, included, and empowered to achieve their full potential.
By bringing unique individuals and viewpoints together, we achieve extraordinary results.
Lam Research ("Lam" or the "Company") is an equal opportunity employer.
Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws.
It is the Company's intention to comply with all applicable laws and regulations.
Company policy prohibits unlawful discrimination against applicants or employees.
Lam offers a variety of work location models based on the needs of each role.
Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex.
'On-site Flex' you'll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week.
'Virtual Flex' you'll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.
