Quantitative Research Associate

Job Title : Quantitative Research Associate Location: Bangalore (IISc Campus) / Hybrid Team: Science & Modeling | Shonku Labs, Alt Carbon Experience: 1–4 years Type: Full-time About Alt Carbon: Alt Carbon is India's leading Carbon Removal (CDR) company.
We blend science, technology & tradition to undertake climate action to turn the clock on historic emissions in the atmosphere.
Our science and technology takes place in Bangalore; our operations are undertaken in Darjeeling's heritage tea estates.
Alt Carbon was started by sibling tea planters Shrey and Sparsh Agarwal, in order to rescue their family's tea estate from being at-risk of climate impact to becoming the frontier of climate action.
Read about our journey below: To read more about our work, check out this news feature about us, go through this twitter thread , or have a look at our work in Darjeeling.
About the Role Alt Carbon is building a new generation of scientific infrastructure to support high-integrity Carbon Dioxide Removal (CDR) through Enhanced Rock Weathering (ERW).
At the heart of this effort is a robust, quantitative approach to modeling weathering reactions, tracking elemental fluxes, and calculating real-world net CO₂ removal.
We are hiring a Quantitative Research Associate to help build, test, and scale the core mathematical and statistical models that underpin our science.
You'll work closely with geochemists, soil scientists, AI engineers, and field teams to turn messy physical data into reliable, registry-grade CDR numbers.
Key Responsibilities Design and validate mathematical models of mineral weathering, geochemical fluxes, and carbon sequestration Conduct uncertainty quantification and error propagation analysis across sampling and modeling workflows Build Monte Carlo simulations and other stochastic models for registry-aligned carbon accounting Analyze datasets from lab, field, and satellite sources to extract meaningful patterns and trends Support the development of sampling strategies , including spatial and temporal optimization Collaborate with product and software teams to embed scientific logic into automated MRV platforms Contribute to whitepapers, publications, and technical memos for third-party validation Qualifications Bachelor's or Master's degree in: – Mathematics / Applied Statistics / Data Science – Physics / Environmental Engineering / Computational Sciences – Or equivalent with strong quantitative background Experience with: – Statistical modeling and inference – Error analysis, confidence intervals, and sensitivity testing – Simulation methods (e.g., Monte Carlo, bootstrapping) Proficiency in Python (preferred) or R for modeling and data analysis Comfort with reading scientific papers and replicating models/equations Bonus: Experience with spatial data, environmental datasets, or climate modeling Bonus: Understanding of basic geochemical or environmental systems (not required — can learn on the job) Who You Are You're a quantitative thinker who gets excited by real-world complexity You treat uncertainty not as a nuisance, but as a solvable scientific problem You're comfortable toggling between equations, field data, and code You're rigorous with numbers, but open-minded to scientific intuition You want your models to matter — in public policy, climate impact, and planetary systems Why This Matters Most of the world's carbon models are built for theory.
You'll help build ones grounded in soil, rock, rain, and real-world data .
This is one of the most consequential modeling problems of our time — and we need people who can handle the complexity.

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