Lynette Robertson
Data Scientist @Nature Harmonics Data Analytics
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WORK HISTORY
Data Scientist @Nature Harmonics Data Analytics
Statistical Methods - Advanced Analytics - Machine Learning || Exploratory Data Analysis - Feature Engineering || Data Visualisation & Reporting || Technical Reporting - Data Journalism || Experimental Design - Hypothesis Testing || Data Collection - Survey Design || Dataset Profiling - Data Quality
EDUCATION
University of Edinburgh - School of Geosciences (Institute of Atmospheric and Environmental Science)
PhD, 'Radon emissions to the atmosphere and their use as atmospheric tracers' (NERC studentship)
Mid Yell Junior High, Yell, Shetland
High School
Anderson High, Lerwick, Shetland
High School
University of Aberdeen
Geography (MA), Physical; Environmental
The University of Edinburgh
MRes, Research in the Natural Environment (NERC studentship)
ABOUT LYNETTE ROBERTSON
I\'m a hybrid data scientist and analyst with a wealth of knowledge and experience of using applied statistics + machine learning to innovate with data, from a career spanning-> pure + applied academic research -> applied science -> freelance data consultancy -> data analytics apprenticeship training (commercial + not-for-profit)With a multidisiplinary background rooted in STEM, I operate from a broad modelling framework which includes supervised and unsupervised ML; numerical simulation of dynamic systems (air quality: Gaussian + Lagrangian); and deep expertise in frequentist statistical methods, including feature engineering, and model evaluation + validation. I have honed my craft from casting the data net deep and wide over 20+y, working with a wide variety of mixed, messy, complex, and high-dimensional datasets, ranging from solely numerical, through to largely categorical and text; working End to End across the full data analytics project life cycle, including guiding data discovery and working as lead statistician. Much of my career has been dedicated to the environment, sustainability (nature-based solutions)+ public health sectors, working with temporal + spatial, and linked cross-sectional + longitudinal intervention environmental epidemiology datasets, applying innovative research methods + analytical techniques to generate real world evidence; actionable recommendations for policy + practice; and high quality peer-reviewed academic publications. In recent years through the course of delivering data analytics apprenticeship training I have gained knowledge and experience of a wide variety of enterprise / business intelligence and operations applications of data analytics, including fraud detection, people analytics, and ESG reporting.I am a strong critical, strategic and divergent thinker, with sharp pattern recognition attention to detail and a firm commitment to scientific rigor Integrity and quality are important to me, and I advocate for the creative evolution of humanity through ethical development and democratisation of machine learning + artificial intelligence technologies amplification of feminine principles in leadership and widespread adoption of a true, regenerative sustainability
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