Lydian

Senior R&D Data Engineer, Hardware Systems

Boston · Senior

Sponsorship not specifiedDetected 4 days ago

PythonRESTData ScienceStatisticsMechanical DesignLabVIEWCommunication

About the role

Our breakthrough technology replaces oil and gas refining with fully electrified, modular, and flexible reactor systems that produce fuel with 95% lower emissions than traditional jet fuel.
Lydian is backed by top climate investors including Congruent Ventures, Galvanize Climate Solutions, Union Square Ventures, Voyager Ventures, Grok Ventures, and Overture.
In particular, we look for tenacious people who are energized by solving the hardest, highest impact problems that come their way.

Build analysis and visualization tools.
Create the data views, plots, and statistical tools that help the team analyze data faster and better.
Program and maintain data-acquisition software (e.g., DAQ Factory, Ignition) so that R&D and pilot test equipment stream reliably into our centralized databases.
Build in data validation checks that quality-control data at the point of acquisition or entry
Run cross-functional analyses. Perform statistical and regression analyses that connect manufacturing and QC data with experimental run data to surface insights across functions
Partner across the technical team. Work with scientists and engineers to understand their data needs and translate them into reliable tools and workflows, and help maintain the IT infrastructure of servers and software systems that house our data
Design and maintain data infrastructure.

5+ years of experience working with data in a hardware, laboratory, or physical-science R&D environment.
Experience only in software absent a physical science/hardware R&D focus is not suitable for this role
Experience with time-series data and relational databases
Proficiency programming for data science, including Python and at least one data-acquisition platform (LabVIEW, DAQ Factory, or Ignition)
Familiarity with data-acquisition hardware and software protocols
The ability to store, connect, and analyze that data well has become central to how quickly we can learn and improve.
BS or MS in an engineering or physical-science discipline, computer science, data science, or a related field
5+ years of experience working with data in a hardware, laboratory, or physical-science R&D environment. Experience only in software absent a physical science/hardware R&D focus is not suitable for this role
A data generalist with demonstrated strengths across data acquisition, infrastructure, visualization, and statistical analysis
Demonstrated success establishing or improving data storage and analysis infrastructure
Comfortable working in a hardware-focused, chemical engineering and materials science data environment
Strong cross-disciplinary communication skills
Ability to independently identify challenges, prioritize work, and implement solutions as the most senior Data professional on Lydian's team

Experience with our current data tools (Notion, Nominal), JMP, or additional data-acquisition platforms (LabVIEW, DAQ Factory, Ignition)
Basic IT and networking skills
Challenging, collaborative, and meaningful work and an important voice in company development
401(k) with company match
Establish structured, systematic data storage across our tools (currently Nominal and Notion, plus others as needs evolve).
Ensure experimental run data (time series), run metadata, and manufacturing and QC data are integrated, consistently formatted, and easy to sort, filter, and query for analysis

Meaningful equity compensation
Unlimited PTO and expectation that all employees take significant time off to rest, recharge, and enjoy life outside work
Excellent insurance with 100% of healthcare, vision, and dental premiums covered for the employee, and 80% coverage for dependents

We are building a motivated, diverse, and collaborative team that is passionate about addressing the growing climate crisis and is committed to excellence in their work.

This listing is sourced directly from Lydian's careers page and normalized into a canonical job model.