Statistician - Manufacturing Environment

Statistician

ABOUT US

We design and produce engineered materials that dramatically increase the energy density of rechargeable Li-ion batteries. We enable smaller, lighter, longer lasting electronic devices and unlock mass adoption of affordable, long-range electric vehicles. Our products work today, are a drop-in solution for existing battery manufacturing processes, and can be manufactured economically at scale.

Sila Nanotechnologies was founded in 2011 and is based in Alameda, CA. Our founders are two early Tesla Motors battery engineers and a Professor of Materials Science at Georgia Tech. We’re backed by top tier venture capital firms, led by Bessemer Venture Partners, Matrix Partners, Sutter Hill Ventures, and In-Q-Tel.

Our company culture is built on personal growth, mutual trust, inclusiveness, and safety. We are building a world-class materials company, where our team is close, debates are data-driven, and our nerdiest office puppies are named Angstrom, Lumen, and Gadget.

We believe that building a diverse team at Sila helps us amplify our individual talents. We are an equal opportunity employer and committed to creating an inclusive environment where good ideas are free to come from anyone. We are proud to celebrate diversity and all qualified applicants are considered for employment without regard to gender, race, sexual orientation, religion, age, disability, national origin, or any other status protected by law.

WHO YOU ARE

You are a creative and collaborative applied statistician. You are skilled in designing experiments and performing statistical analysis to create process specifications. You can communicate technical details to diverse audiences, especially in relation to data quality and result interpretation. You like challenges, you take pride in your work, and can manage multiple projects simultaneously while delivering results on time. You’re passionate about clean technology and are excited to be a key contributor to set the standard for the next generation of Li-ion batteries.

Statistical Knowledge (DOE & Spec Setting):

Demonstrated knowledge in design of experiments (DOE). Proficient in screening designs, linear model development, and response surface methods (RSM); experience with developing, monitoring, and updating both control and specification limits in a six sigma environment

Statistical Knowledge (Process Monitoring):Experience with developing and implementing product and process monitoring systems and strategies

Statistical Knowledge (MSA & Equivalence):

Experience designing and analyzing measurement system analysis and equivalence studies

Software Skills:

Strong familiarity with statistical software packages (eg, JMP) and associated data systems; able to move quickly between different physical tools, databases, data processing software and operating systems to effectively generate and share insights; able to work with large data sets; familiarity with MVDA platforms (eg, SIMCA) preferred

Standards & Guidelines:

Familiarity with AIAG and/or ISO guidelines for statistical process control and measurement system analysis

Leadership & Autonomy:

Can effectively manage multiple responsibilities, is self-motivated and can work autonomously with minimal guidance; comfortable with ambiguity and eager to create structure

Teamwork & Growth:

Able to collaboratively solve problems and set others up for success; knowledgeable, curious, and humble; easy rapport, genuine empathy, and durable composure

Communication:

Excellent verbal and written communication skills; strong presentation skills and able to distill complex problems and data into understandable and actionable content

Cross-functionally collaborate with Process Development, Manufacturing, & Quality teams to develop designed experiments (DOE). 

Guide process development and statistical process control (SPC), including setting process specification limits and improving process capability (CPK).

Provide guidance for statistical techniques (including intermediate precision and gauge R&R) to quantify measurement precision and accuracy.

Utilize statistical methods to aid in design and operational decision making

Work closely with the Process Development team to establish baseline performance metrics and identify the key levers to drive optimal process reliability, quality and uptime.

Develop efficient methods to munge raw SCADA outputs to organized and useful data.