Conrad MacCormick - Principal Data Scientist

Conrad MacCormick

Data

About

I'm a dedicated data professional with over 13 years' experience in data science and statistical roles. During this time, I've honed my skills in leveraging data to drive actionable insights and solve complex problems.

I have strong time management and a solid work ethic, with a drive to produce high-quality work and meet deadlines. My customer focus comes from years working with diverse stakeholders — especially during my time as a data science consultant. I work to understand client needs and collaborate closely to create solutions that deliver real value.

Currently serving as Principal Data Scientist at Nicholson Consulting, I lead and oversee data projects, manage teams of 3–5 data scientists, and support two staff members as their people manager. I'm also an accredited Integrated Data Infrastructure (IDI) researcher with nearly 10 years' experience working with this powerful dataset.

Experience
13+ Years
IDI Research
10 Years
Team Leadership
3+ Years

Experience

Principal Data Scientist

November 2021 – Present
Nicholson Consulting

Leading data and analytical projects in close collaboration with the CEO and other Principal Data Scientists. Typically manage project teams of 3–5 data scientists, whilst also serving as people manager for two staff members. Use technical expertise to support the team in improving their proficiency across a wide range of analytical tools, enabling them to generate valuable insights for diverse audiences, including non-technical stakeholders.

Senior Data Scientist

September 2019 – November 2021
Nicholson Consulting

Took a lead role collaborating with clients to deliver analytical projects — primarily using the Integrated Data Infrastructure (IDI).

Data Analyst

January 2018 – September 2019
Electricity Authority

Analysed and built models using New Zealand electricity data. Gained experience with complex optimisation modelling techniques.

Senior Analyst

April 2016 – December 2017
Social Investment Agency/Unit

Seconded as Senior Analyst following the transformation of the Social Sector Investment Change Programme into the Social Investment Unit (April 2016), and subsequently the Social Investment Agency (July 2017). Used Statistics NZ's Integrated Data Infrastructure (IDI) to explore and understand the effectiveness of government investment in social services.

Analyst

October 2015 – March 2016
Social Sector Investment Change Programme

Coordinated and helped project-manage a software proof-of-concept, including stakeholder engagement with NGOs and various government agencies.

Analyst

September 2015
New Zealand Treasury

Seconded to Treasury's Analytics and Insights team to analyse and understand the life pathways of young New Zealanders using Statistics New Zealand's Integrated Data Infrastructure (IDI).

Forecasting and Costing Analyst

January 2015 – December 2017
Ministry of Social Development

Responsible for regular forecasting of Vote Social Development appropriations and cost modelling of benefit-related policies.

Statistical Analyst

October 2011 – December 2014
Statistics New Zealand

Worked in the Statistical Methods unit, utilising expertise across a variety of methodological areas.

Skills & Proficiency

R & Shiny Expert
SAS Expert
Statistical Analysis Advanced
Project Management Advanced
SQL Advanced
JavaScript & HTML Proficient
Git Proficient
Python Intermediate

Education

BSc in Geography
Victoria University of Wellington
2007 – 2010

Interests

  • Being with my son
  • Playing guitar
  • Reading
  • Programming
  • Data visualisation

Published Work

R Playground

Try some basic R visualisation. This example creates a scatter plot showing the relationship between life expectancy and GDP per capita for selected countries in 2007. Feel free to modify it or write your own code.

# Pre-loaded libraries # Create a scatter plot with ggplot2 library(ggplot2) library(gapminder) # Filter to 2007 data and select interesting countries countries <- c("New Zealand", "Australia", "United States", "China", "India", "Germany", "Brazil", "Japan") gap_2007 <- gapminder[gapminder$year == 2007 & gapminder$country %in% countries, ] # Create the plot ggplot(gap_2007, aes(x = gdpPercap, y = lifeExp, colour = country, size = pop)) + geom_point(alpha = 0.7) + scale_size(range = c(3, 15), guide = "none") + scale_x_log10(labels = scales::dollar_format()) + labs(title = "Life Expectancy vs GDP Per Capita (2007)", x = "GDP per capita (log scale)", y = "Life expectancy (years)", colour = "Country") + theme_minimal() + theme(legend.position = "right", plot.title = element_text(size = 14, face = "bold"))

Side Projects

The following are older personal projects from several years ago, maintained here for archival purposes.