I’m finishing my M.S. in hydrology and trying to translate thesis work into resume bullets for water resources roles: I calibrated SWAT with SUFI-2 on a 1,200 km2 basin, processed NLCD/SSURGO in Python, and hit NSE 0.78 under 10-fold cross-validation. For those who’ve landed interviews, do you emphasize uncertainty analysis and reproducible workflows (GLUE, bootstrapping, Git/Jupyter) or stick to decision-relevant outcomes like predicted peak flow reductions under BMP scenarios?
@OP Interviewers perked up when I showed ‘reproducible Git/Snakemake’ and a SWAT repo; NSE okay, uncertainty plots mattered.
Quick example: I started getting callbacks after I rewrote a bullet to “Automated SWAT+SUFI-2 with Git/Snakemake; 95PPU covered 82% of flows; NSE 0.78; reruns via ‘make reproduce’,” and then told one concrete outcome in the interview — @OP, I tied it to “prioritized two BMPs that cut P load about 12%.” Keep the deep uncertainty plots in a linked portfolio or repo, but keep the resume line tight and decision-focused.