{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:49:03Z","timestamp":1782920943959,"version":"3.54.5"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/publication-rights-and-licensing-policy"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGecom Exch."],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:p>In our years as applied scientists and managers at Google, Amazon, and Meta, we have seen both the strengths that EconCS researchers can leverage in industry, as well as common challenges that these researchers face. Most EconCS PhD programs do not emphasize exploratory data analysis, applied machine learning and statistics, or a coding mindset, even though these are valuable skills to have in industry. In this article we share how these skills are leveraged, and how you can invest in building these skills now. In doing so, we hope to make it easier for people from the EconCS community to be successful in industry, be it during an internship, a sabbatical, as a part-time consultant, or as a full time applied scientist!<\/jats:p>","DOI":"10.1145\/3821553.3821557","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:16:00Z","timestamp":1782918960000},"page":"15-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["EconCS in Industry: Skills to Succeed as an Applied Scientist"],"prefix":"10.1145","volume":"23","author":[{"given":"Nikhil R.","family":"Devanur","sequence":"first","affiliation":[{"name":"Meta"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renato Paes","family":"Leme","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okke","family":"Schrijvers","sequence":"additional","affiliation":[{"name":"Central Applied Science, Meta"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"-S","author":"Angrist J. D.","year":"2009","unstructured":"Angrist, J. D. and Pischke, J.-S. 2009. Mostly harmless econometrics: An empiricist's companion. Princeton university press."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Athey S. and Imbens G. W. 2017. The econometrics of randomized experiments. In Handbook of economic field experiments. Vol. 1. Elsevier 73\u2013140.","DOI":"10.1016\/bs.hefe.2016.10.003"},{"key":"e_1_2_1_3_1","first-page":"4","article-title":"The movielens datasets: History and context","volume":"5","author":"Harper F. M.","year":"2015","unstructured":"Harper, F. M. and Konstan, J. A. 2015. The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 4 (Dec.).","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2649-2"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2007.55"},{"key":"e_1_2_1_6_1","unstructured":"Kleppmann M. 2019. Designing data-intensive applications. English."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Kohavi R. Tang D. and Xu Y. 2020. Trustworthy online controlled experiments: A practical guide to a\/b testing. Cambridge University Press.","DOI":"10.1017\/9781108653985"},{"key":"e_1_2_1_8_1","volume-title":"Python for data analysis: Data wrangling with pandas, numpy, and jupyter","author":"McKinney W.","unstructured":"McKinney, W. 2022. Python for data analysis: Data wrangling with pandas, numpy, and jupyter. O'Reilly Media, Inc."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_2_1_10_1","volume-title":"The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track.","author":"Su K.","unstructured":"Su, K., Huo, Y., Zhang, Z., Dou, S., Yu, C., Xu, J., Lu, Z., and Zheng, B. 2024. Auctionnet: A novel benchmark for decision-making in large-scale games. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_2_1_11_1","volume-title":"Python data science handbook: Essential tools for working with data. O Reilly Media","author":"VanderPlas J.","unstructured":"VanderPlas, J. 2016. Python data science handbook: Essential tools for working with data. O Reilly Media, Inc."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.03021"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 9th Python in Science Conference, St\u00e9fan van der Walt and Jarrod Millman, Eds. 56 \u2013 61","author":"Wes McKinney","year":"2010","unstructured":"Wes McKinney. 2010. Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, St\u00e9fan van der Walt and Jarrod Millman, Eds. 56 \u2013 61."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v059.i10"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01686"},{"key":"e_1_2_1_16_1","volume-title":"R for data science","author":"Wickham H.","unstructured":"Wickham, H., Grolemund, G., et al. 2017. R for data science. Vol. 2. O'Reilly Media, Inc."},{"key":"e_1_2_1_17_1","unstructured":"Xu A. 2020. System design interview: An insider's guide. Independently published."}],"container-title":["ACM SIGecom Exchanges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3821553.3821557","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:16:10Z","timestamp":1782918970000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3821553.3821557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,7,1]]}},"alternative-id":["10.1145\/3821553.3821557"],"URL":"https:\/\/doi.org\/10.1145\/3821553.3821557","relation":{},"ISSN":["1551-9031"],"issn-type":[{"value":"1551-9031","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"2026-07-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}