Job Description

About TrelliSense

TrelliSense is on a mission to revolutionize environmental and operational intelligence by transforming how Greenhouse gas emissions are detected, quantified, and understood. Using cutting-edge optical spectroscopy and advanced analytics, we empower emitters and project developers with real-time, accurate insights to drive meaningful operational efficiency and emissions reductions.

Position Overview

We are seeking a highly skilled and mission-driven Analytics Lead to architect, develop, and scale our emissions analytics and estimation platform. In this role, you will be responsible for leading data science and analytics initiatives that turn complex spectroscopic sensor data into actionable emissions insights. You will work closely with domain experts, engineers, and external stakeholders to design algorithms, build models, and ensure scientific rigor in emissions quantification.

This role is an exciting opportunity to get your hands dirty in massive emissions datasets and build elegant algorithms that provide unique insights to help customers protect their gas and drive meaningful emissions reductions.

Key Responsibilities

• Lead the development of data models and algorithms to estimate emissions from continuous optical spectroscopic data collected at customer sites
• Build and maintain robust data pipelines and analytics frameworks to ingest, clean, and analyze high-dimensional sensor data combined with meteorological data
• Design and validate methods for source attribution, concentration quantification, and uncertainty estimation
• Collaborate cross-functionally with hardware, software, and product teams to ensure integration and scalability of analytics solutions
• Translate complex analytical results into clear, actionable insights for both technical and non-technical audiences
• Drive innovation by exploring new statistical, ML, and physical modeling approaches to improve emissions estimation
• Provide technical leadership and mentor junior staff as the team grows

Required Qualifications

• Advanced degree (M.S. or Ph.D.) in Atmospheric Science, Physics, Engineering, Data Science, or a related quantitative field
• 5+ years of experience in data analytics, modeling, or estimation, preferably in environmental, energy, or industrial applications
• Strong knowledge of optical spectroscopy methods and their application to gas detection and quantification
• Strong knowledge of and 2+ years of experience working with atmospheric modeling techniques
• Proven expertise in Python and data processing frameworks (e.g., Pandas, NumPy, SciPy, scikit-learn, PyTorch/TensorFlow)
• Experience with cloud computing and data infrastructure (especially AWS)
• Solid understanding of uncertainty quantification, calibration, and signal processing techniques
• Excellent communication skills and the ability to convey technical concepts to diverse audiences

Preferred Qualifications

• Experience with emissions inventory development, regulatory reporting frameworks, or remote sensing data
• Familiarity with field deployments and real-world measurement campaigns
• Familiarity with LabVIEW
• Familiarity with forward and inverse analytical techniques
• Prior experience in a startup or fast-paced R&D environment

Why TrelliSense?

• Join a small and passionate team tackling one of the world’s most gnarly challenges - methane emissions
• Work with cutting-edge technology at the intersection of spectroscopy, atmospheric modeling and environmental science
• Flexible working environment and opportunities for professional growth
• Competitive compensation, equity, and benefits package including insurance (health, dental, vision) and 401(k) with company match



Apply Now

We are a small team, and we rely on the passion of each teammate to successfully commercialize a novel solution that is truly beyond the industry standard. If you're excited to lead groundbreaking analytics work that can make a measurable impact on our planet, we'd love to hear from you!

Salary range: $75k - $140k

Applications will be accepted on a rolling basis until the position is filled.

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