Ryan Jenkinson CV

Note: I only work in technology, data science and software development roles in the climatetech space. Ideally in smaller teams where I can make a greater impact.

An experienced data professional capable of leading high performing, mission-driven teams across the tech value stack. I have over 6 years of industry experience applying data science and engineering, AI & ML, technical leadership and strategy for organisations of all sizes. I am hands on, capable of taking things from zero-to-one, delivering major projects in a principled way and mentoring others. My focus is on tackling climate change. A continual learner who is highly articulate with the confidence and experience to communicate and influence effectively at all levels. I build strong productionised software and craft data-driven narratives. Experienced at giving talks and contributing to discussions on climate and tech, e.g at COP26.

ryan.eco - ryanjenkinson - ryanjenkinson - @ryancjenkinson - hello@ryan.eco

Core Competencies

Technical
I have a broad full-stack knowledge of backend systems, using Python, SQL and cloud technologies.
  • Data platforms, algorithm development and modelling pipelines (machine learning and artificial intelligence)
  • Data science and analysis
  • Software development for cloud
  • Modelling of energy systems, with a focus on electrification
Leadership
I am passionate about leading high-performing impact-driven teams in a hands-on way.
  • Building and supporting world-class organisations with strong cultures
  • Delivering high-impact products and analyses in an incremental and continuous way
  • Formulating wider strategies rooted in technology and first-principles thinking that maximise value
  • Driving transparent, inclusive and comprehensive decision making
  • Mentoring small mission-driven teams, upskilling technical knowledge

Relevant Experience

Senior Data Scientist, Kraken (Octopus Energy Group)
(August 2023 - Present)

Working on putting consumers at the heart of the energy system, by automating and optimising domestic low carbon technologies globally (solar + home batteries, heat pumps, electric vehicles, smart thermostats etc) to help balance the grid while maintaining household preferences.

  • Responsible for maintaining and improving production code that optimises 100,000s of assets globally each day
  • Leading cross-organisational data science best practices, including MLOps pipelines
  • Working closely with external partners and grid operators in multiple countries to deliver and showcase the value of automation and optimisation in the grid

Head of Data and Chief of Staff, Centre for Net Zero (Octopus Energy Group)
(June 2021 - August 2023)

A founding early hire to a new mission-driven autonomous research unit of Octopus Energy, with roles and responsibilities growing as the startup evolved:

  • Built a modern data stack platform and automation workflows using dbt, GCP and infrastructure-as-code methodologies for a small data team, with 100GBs of data from a variety of public and private data sources.

  • Led and delivered multiple high-impact research projects increasing our understanding of how consumers could adopt and use low carbon technologies, managing the data team. Examples include “world first” large-scale rigorous experimentation into domestic intelligent demand (demand side response) and creating a scalable generative AI model that generates realistic smart meter data, partnering with the Linux Foundation Energy.

  • Research published in key journals and analysis featured in high profile influential trades, newspapers and conferences (e.g COP26).

  • Stakeholder management with Grids (TSO, DSOs) and influencing policymakers (Energy Departments, Regulators) globally - representing the organisation externally and responsible for £million innovation funding.

  • Hiring in key strategic positions, and responsible for broader data strategy and technology decisions.

Lead Data Scientist, Optimise Prime, Hitachi Social Innovation
(March 2020 - June 2021)

I led a team of 6 people (inc. contractors) on worlds largest commercial EV trial (Optimise Prime), a £35m. Ofgem energy Innvoation project with partners Centrica, Uber, Royal Mail, UKPN & SSEN.

  • Led technical delivery of 3 workstreams, analysing billions of datapoints from over 6,000 vehicles to drive the transition from petrol/diesel to electric. Specifically:
    • Built a powerful ML prediction model to recommend optimal chargepoint location in London for EV drivers, with quarterly updates to Uber and a talk delivered at Big Data LDN 2020. Resulted in the strategic placement of highly utilised on-shift chargepoints, greater driver confidence in EVs and Uber Green.
    • Analysed behavioural patterns of different commercial fleets, recommending optimal practices and vehicles to deploy as they transition to electric.
    • Delivered over £2m/yr in operational savings for Royal Mail across 10 depots through provisioning of smart charging systems and forecasting models to respond to flexibility events, ensuring risk and cost minimsation.
  • Deployed modern cloud based web app for a GB distribution network operator that reduces time for depot managers to purchase electric vehicles and secure grid connections with their DNO. This reduced potential network reinforcement and grid connection costs to the tune of £millions/year.
  • Mentored 2 Data Scientists. Interviewed candidates for Data Science positions.

Various AI / ML Research and Consultancy Roles
(July 2018 - March 2020)

  • Defining AI strategy at Digital Catapult, delivering projects to optimise logistics for a global company and managing a consortium bid to optimise carbon and energy profiles for UK agriculture.
  • Implementing a production, state-of-the-art natural language understanding model for a startup analysing millions of datapoints per month.
  • Worked on time-series and computer vision models for an independent consultancy in the security sector, owning 2 patents.

Education

2018 - 2019
MSc, Computational Statistics & Machine Learning (Distinction); University College London (UCL)
  • (Industry) Thesis title: Multitask & Meta Learning for Aspect based Sentiment Analysis (NLP) with applications in startups
  • Specialisms: Advanced Deep Learning and Reinforcement Learning (taught by Google Deepmind), Natural Language Processing, Computer Vision, Statistics, Data Science & Analysis
2015 - 2018
BA Mathematics (2.i); Girton College, University of Cambridge
  • Specialisms: Statistics, Optimisation, Quantum Mechanics, Computability Theory
2011 - 2015
A Levels (3A*), GCSE’s (10A*,1A); Wilmslow High School
  • A2 Levels : Mathematics A*, Further Mathematics A*, Physics A* (self-taught)
  • AS Levels : Psychology A, Business Studies A

Projects

ryan.eco open-source website
I have a website, to build my personal brand in the “EnTech” space. Features include:
  • About Me, CV, Blog
  • CI/CD Deployment
Demand Flexibility Service App (open-source)
An app that I built in public using open data to showcase the benefits of the worlds largest consumer flexibility in-market trial (ran by National Grid ESO over Winter 2022/23):
  • Over 1,000 unique app users, including key grid stakeholders
  • CI/CD Deployment

Technical Competencies

Data Science
I use Python exclusively for Data Science, though I have familiarity with R and MATLAB.
  • Core libraries (Numpy, Pandas)
  • Data Visualisation (Matplotlib, Seaborn, Plotly, Altair)
  • Machine Learning (Sklearn, Tensorflow, Pytorch, Huggingface)
  • Graph-based methods (NetworkX)
  • Geospatial Analysis (Geopandas, web.gl / Mapbox)
  • Big Data (Spark, Dask)
  • Numerical Optimisation (Scipy, Linear Programming in PuLP)
Data Infrastructure
I am conversent in a range of backend data infrastructure toolkits, that follow the modern ELT data stack.
  • Infrastructure as Code (Terraform)
  • ELT Modern Data Stack (Airflow, Google Cloud, BigQuery, dbt)
Software Development
I am passionate about building strong software products with best practices and standards. Linux-based.
  • Version Control (git)
  • Package management (brew, poetry, pipenv, pip)
  • IDEs (VSCode, Jupyter Notebooks, vim)
  • Cloud (GCP, AWS, Azure)
  • Containerisation (Docker, Google Artifact Registry)
  • CI/CD (Github Actions)
  • API Frameworks (Django, FastAPI)
  • Applications (Streamlit)
  • Testing (Pytest)

Notable Talks & Events

  • [Nov 2021] COP26 Fringe Event: agent based approaches for reducing emissions
  • [Oct 2022] DataFest Fringe Event: using open source data to support decarbonisation (in collaboration with NESTA)
  • [Mar 2022] Energy Institute Young Energy Professionals - Flexibility and Demand Side Response: How demand side response is enabling a cleaner, cheaper, and smarter energy system