My STOR-i So Far

DiverSTOR-i Talk
14 November 2023

Nicola Rennie

What’s this talk about?

  • My career path

  • My experience of being a woman in data science

  • My advice to my former self

My career path

Before STOR-i

  • BSc (Hons) in Mathematics

  • Not a lot of statistics…

  • Thought I should learn some more before I got a job

Photograph of St Andrews

STOR-i

Group photo standing on the stairs



So I joined STOR-i in October 2017.

How it started…

Photo of Catherine Cleophas

Catherine Cleophas

Photo of Florian Dost

Florian Dost

Title: Data-Driven Alerts in Airline Revenue Management

Industry partner: Lufthansa (Jonas Rauch)

Lufthansa logo

How it ended…

Photo of Catherine Cleophas

Catherine Cleophas

Photo of Florian Dost

Florian Dost

Photo of Adam Sykulski

Adam Sykulski

Title: Detecting demand outliers in transport systems

Industry partner: Deutsche Bahn (Philipp Bartke and Valentin Wagner)

Deutsche Bahn logo

How it ended…

  • Gained a (statistics) PhD supervisor
  • Found a second community of researchers
  • Trains are more complicated…

PhD at STOR-i

Network diagram

Time series examples

  • Online functional outlier detection

  • Extending to a network setting

  • Empirical studies with railway and bike-sharing data

  • Finished ~ August 2021

Life after STOR-i

  • When I started at STOR-i, it was with the intention of going into industry

  • Data science consultancy seemed like a good idea

Mathematical Consultant at Smith Institute

Smith Institute logo

  • Mostly statistical advice projects

  • Left after three months

Data scientist at Jumping Rivers

Jumping Rivers logo

  • Data science consultancy

  • Data engineering

  • Posit (formerly RStudio) licensing

  • Training courses

Data scientist at Jumping Rivers

  • The initial offer

  • Fully-remote

  • Mostly R development

  • A lot of project management

  • Developing and running training courses

Looking for something new…

Did I really want another data science consultancy job?


No.

Looking for something new…

Screenshot of tweet

Lecturer in Health Data Science at CHICAS

  • Teaching and scholarship post

  • The interview process

  • Teaching, research, supervision, and engagement

  • Research in collaboration with NHS

CHICAS logo

My experience of being a woman in data science

R-Ladies

R-Ladies is a worldwide organisation whose mission is to promote gender diversity in the R community.

The R community suffers from an under-representation of minority genders (including but not limited to cis/trans women, trans men, non-binary, genderqueer, agender) in every role and area of participation, whether as leaders, package developers, conference speakers, conference participants, educators, or users.

RLadies logo

PyLadies does a similar thing in the Python community.

I recently announced R-Ladies Lancaster was back!

Two (paraphrased) responses:


Why are you creating an R meetup that men can’t attend?


Why are you creating an R meetup that men can attend?

Why do we need more gender diversity in the R community?

Douglas Bates

John Chambers

Peter Dalgaard

Robert Gentleman

Kurt Hornik

Ross Ihaka

Tomas Kalibera

Michael Lawrence

Brian Ripley

Friedrich Leisch

Uwe Ligges

Thomas Lumley

Martin Maechler

Sebastian Meyer

Paul Murrell

Martyn Plummer

Deepayan Sarkar

Duncan Temple Lang

Luke Tierney

Simon Urbanek

Heiner Schwarte

Guido Masarotto

Stefano Iacus

Seth Falcon

Duncan Murdoch

Martin Morgan

How do we fix it?


Women are often over-mentored and under-sponsored.


-Julia Silge

Photo of Catherine Cleophas

Catherine Cleophas

Photo of Rhian Davies

Rhian Davies

Photo of Jo Knight

Jo Knight

Photo of Anastasia Ushakova

Anastasia Ushakova

Photo of Alice Ashcroft

Alice Ashcroft

Photo of Xiaoyun Chen

Xiaoyun Chen

Photo of Eleanor D'Arcy

Eleanor D’Arcy

Photo of Robyn Goldsmith

Robyn Goldsmith

While we’re talking about R-Ladies…

My advice to my former self

Advice to my former self

  • Choose a supervisor(s) you get on with.

  • Find a mentor: outside perspective is good.

  • It’s easier to get a job when you have a job.

  • Try different things to find out what matters to you.

What matters to me?

  • Working with good people

  • Doing interesting things

  • Making things that are useful

It rarely goes according to plan
and that’s okay.