Data Scientist

Sophia Qian Niu

E-mail: **********@*****.*** Cell: 314-***-****

Summary

Graduated Ph.D. in biological sciences. Have deep understanding and proficient application on inferential statistics with peer-reviewed publication record. Motivated and problem-driven individual, and passionate about assembling, exploring, analyzing, as well as presenting data. Seeking positions working on data-driven business with real-world impact.

Education

Ph.D. in Ecology, Saint Louis University, St. Louis, MO Sep 2015 M.Phil. in Biological Sciences, The Hong Kong University, Hong Kong, China Sep 2009 B.Sc. in Environmental Sciences, Nankai University, Tianjin, China July 2006

Skills

Specialtie: Experimental design, inferential statistics, generalized linear models, practical machine learning.

Analytical tools: proficient use of R, R markdown, ggplot2, Shiny App. and ArcGIS; familiar with Python, Octave, and git.

Projects

Multiple Regression Analysis for Hydrology-Ecology Associations: Predictive models involving spatial and environmental variables are constructed to predict attributes of aquatic species; AIC approaches have been applied for model selection, cross-validation applied for model evaluation.

Bayesian Mixing Model for Resource Allocation: I have used Bayesian stable isotope mixing model to estimate relative contribution of different food resources in aquatic species.

Niche Modeling: I have characterized habitat use by common cray sh species, and predict their geographic distribution within a GIS and niche modeling framework.

Environmental Flow Determination: A have conducted a Before-After Control-Impact(BACI) design for field data collection, and established an empirical model to inform local dam operation for conservation purpose.

Impacts of Mosquito Control Agents: A randomized design field experiment, and ANOVA was conducted to test the impacts of widely used mosquito control methods on aquatic food webs.

Grants and Awards

Dissertation Fellowship, Saint Louis University (2014{15) Top 5% Ph.D. candidates.

Doctoral Dissertation Improvement Grant, National Science Foundation (2013-14) Top 10% Ph.D. candidates in Biological Sciences.

Relevant course work

Biostatistics; GIS Biology; Bioinformatics using Python (with Coursera);

Inferential Statistics, R Programming, Regression Models, Practical Machine Learning, and Developing Data Products - Data Science Specialization track with Coursera. 1

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