Applied scientist resume template

Applied science careers carry two records — papers and production — and most templates honour only one. This serif design gives publications a citation-styled section of their own while experience bullets stay quantified and parseable, so the resume survives both the hiring manager and the screening software.

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✓Applied and research scientists in industry
✓PhD/MS researchers moving from academia to product teams
✓Data scientists with publications worth showing

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✓Scored 93/100 — interview-ready against the 30 published rules, measured by exporting this exact template. Certified: we only badge templates at 90 or above, a stricter bar than the 85 our checker calls interview-ready, so the design leaves headroom for your own content.

Dr. Rhys Payne

Applied Scientist, NLP and Retrieval

rhys.payne@example.com·(206) 555-0341·Seattle, WA·scholar.google.com/citations?user=rhpayne·github.com/rhpayne

Summary

Applied scientist who takes retrieval and ranking research into production. Four first-author publications, two systems serving 30 million queries a day, and the offline-to-online gap closed to under a point on the last three launches. Works from the metric backwards: what the user sees, then the model that earns it.

Experience

Applied Scientist II — Nimbus AI Research

09/2022 – Present

Built the hybrid dense-sparse retrieval stack for product search serving 30 million queries a day; nDCG@10 up 4.1 points
Distilled the ranking cross-encoder into a 40 ms student model at 98% of teacher quality; serving cost down 63%
Published the hard-negative mining method behind it at EMNLP 2025 and open-sourced the training recipe (1,900 stars)
Designed the offline evaluation suite (12 query slices, interleaving harness) now required before any ranking launch
Led a 4-person team through three launches; each shipped within one point of its offline estimate

Ph.D. Researcher — Carnegie Mellon University, Language Technologies Institute

08/2017 – 08/2022

Thesis on efficient retrieval under distribution shift; three first-author papers at SIGIR, EMNLP and a NeurIPS workshop
Built a 200 million document evaluation corpus with domain and time splits, adopted by two other labs
Taught the graduate search engines course as head TA for four semesters (110 students a term)
Reviewer for SIGIR, EMNLP and ACL; area chair for the TREC deep learning track in 2022

Research Intern, Search Science — Orbital Commerce

05/2020 – 08/2020

Prototyped query rewriting with a sequence-to-sequence model; null-result rate on long-tail queries down 17% in A/B test
Wrote the internal report that became the team's 2021 roadmap item and a WSDM industry paper

Skills

Dense and sparse retrieval·Learning to rank·Knowledge distillation·Offline and online evaluation·Interleaving and A/B design

Python·PyTorch·FAISS·Spark·Kubernetes·C++ for serving paths

Experiment design·Technical writing·Mentoring·Cross-team launch reviews

Education

Ph.D. Language and Information Technologies — Carnegie Mellon University2017 – 2022

Thesis: Efficient neural retrieval under distribution shift

B.S. Computer Science, summa cum laude — University of Washington2013 – 2017

Selected Publications

Payne, R. et al. Hard negatives at scale for dense retrieval. EMNLP 2025.

Payne, R. et al. Retrieval under temporal shift: a 200M-document benchmark. SIGIR 2021.

Payne, R. et al. Query rewriting for long-tail product search. WSDM 2021, industry track.

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Do publications belong on an industry resume?

Yes — three selected, citation-styled, with venues named, after your experience. Industry readers scan them for rigour, not completeness; link a Scholar profile for the rest.

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