About Me
I am a dedicated and experienced data professional with a passion for exploring, analysing, and solving problems using data. I have over 13 years’ experience in data science and statistical roles and nearly 10 years as an accredited Integrated Data Infrastructure (IDI) researcher. During this time I have honed my skills in leveraging data to drive actionable insights.
I have good time management and a strong work ethic with a drive to produce high-quality work and meet deadlines. I have a strong customer focus with experience working with a range of stakeholders – especially from my time as a data science consultant. I look to understand a customer’s needs and work closely with them to create a product that will meet those needs.
Currently serving as an Analytics Lead at Nicholson Consulting, I play a key role in leading and overseeing data projects. I bring a versatile skill set and proficiency in a wide range of analytical software, enabling me to effectively transform raw data into valuable insights that resonate with diverse audiences, including non-technical stakeholders.
Experience
As an Analytics Lead at Nicholson Consulting, I play a key role in leading data and analytical projects working closely with the CEO and other Analytics Leads. Most project teams I lead range in size from 3-5 data scientists. As well as this, I am a people manager for 2 staff members. Using my technical expertise, I support my team to improve their proficiency in a wide range of analytical software, enabling them to generate valuable insights that resonate with diverse audiences, including non-technical stakeholders.
As a Senior Data Scientist at Nicholson Consulting, my duties involved having a lead role collaborating with clients in delivering various analytical projects – primarily using the IDI.
As a Data Analyst at the Electricity Authority, my duties involved analysing and building models using New Zealand electricity data. I gained experience using complex optimisation modelling techniques.
In April 2016, the Social Sector Investment Change Program became the Social Investment Unit and, in July 2017, the Social Investment Unit became the Social Investment Agency. I was seconded as a Senior Analyst. My responsibilities in this role included using Statistics NZ's Integrated Data Infrastructure (IDI) to explore and understand the effectiveness of government investment in social services.
From October 2015 I was part of the Social Sector Investment Change Programme (SSICP). My role involved coordinating and helping to project manage a software proof-of-concept which included stakeholder engagement with both NGOs and a variety of government agencies.
In September 2015 I was located at the Treasury to work with the Analytics and Insights team. I helped to analyse and understand the life pathways of young New Zealanders using Statistics New Zealand’s Integrated Data Infrastructure (IDI).
My role at the Ministry of Social Development was as a Forecasting and Costing Analyst. My responsibilities included regular forecasting of Vote Social Development appropriations and cost modelling of benefit-related policies.
I worked as a Statistical Analyst at Statistics New Zealand. I worked in the Statistical Methods unit where my role utilised my expertise in a variety of methodological areas.
Projects
The following are a collection of side projects I previously worked on in my spare time.
rmarkdown to visualise the results from the WRUG community questionnaire.
Skills & Proficiency
R & Shiny
SAS
Statistical Analysis
Project Management
SQL
JavaScript & HTML
Git
Python
Node.js
Published Work
R Playground
# This will get executed each time the exercise gets initialized
# Have a go!
library(ggplot2)
library(dplyr)
ggplot(mpg, aes(reorder(class, class, function(x) length(x)))) +
geom_bar(fill = "grey", width = 0.6) +
geom_bar(data = filter(mpg, class == "suv"),
fill = "steelblue", width = 0.6) +
coord_flip() +
ggtitle("SUVs make up the largest number of cars in this dataset!") +
xlab("Class") +
theme_minimal() +
theme(panel.grid = element_blank(),
panel.background = element_rect(fill = "transparent",
colour = NA),
plot.background = element_rect(fill = "transparent",
colour = NA),
axis.line.x = element_line(colour = "grey"),
axis.ticks.x = element_line(colour = "grey")
)