Agreena is an exciting, dynamic, and purpose-oriented organisation united in a mission to mobilise farmers and corporations to unlock the value of nature and help restore the planet. While we’re rooted in agriculture, finance, and technology, our team of experts range from soil carbon scientists and software developers to market strategists and regulatory affairs experts. Over 230 employees across more than 40 nationalities are gathered under the common Agreena flag – either working from our headquarters in Copenhagen, offices in London, or remotely across Europe.
As a rapidly scaling climate agtech, Agreena provides solutions that drive both environmental and financial sustainability in farming. We have helped farmers across 20 countries in their journey from conventional agriculture to regenerative farming practices to reduce greenhouse gas emissions and remove CO2 from the atmosphere, storing it in soil. Our company offers a supportive and positive work environment with opportunities for learning, leading and growth no matter where you are in your professional journey. We believe in giving our employees a lot of responsibility, and we encourage new thinking, innovation and fun.
We are looking for an in-house statistician to assist our Programme and Data teams. Across both, we are looking to better understand and explain our data in order to bolster our scientific rigour. You will sit between both teams and should be confident working with different types of statistical analysis, process-based modelling, and sampling/stratification.
Equally, you should have a good understanding of statistical modelling and bring a fresh perspective to our machine learning and process-based approaches. Thus, you will be a strong communicator capable of translating complex analysis into simplified insights to both internal and external stakeholders.
The Programme and Data teams are integral to the scientific operations of the company, and you will work closely together with them and our Product teams to ensure state of the art science drives our world leading carbon platform. As such, you will be part of a dynamic and high-paced work environment constantly subject to innovation.
How Will You Make An Impact
- Estimating uncertainty and error propagation in our models;
- Applying Bayesian methods for the calibration of complex process-based models;
- Designing sampling strategies for our data collection campaigns;
- Evaluating the quality and quantity of our data;
- Communicating and visualising key insights;
Who We’re Looking For
- Self-driven individual capable of collaborating in a multi-disciplinary environment;
- Minimum Bachelors in Statistics, Mathematics, or appropriate science/engineering field;
- 3+ years of experience working in a relevant field, with experience in some or all of the following:
- Statistical and data-sampling techniques such as regression, imputation, random forest, Monte Carlo, stratification, and/or clustering;
- Working with temporal and spatial data;
- Experience coding in R;
- Strong scientific writing, report creation and communication skills to audiences with a range of technical expertise.
Bonus Points If You Have
- Masters or PhD in one of the relevant Science fields mentioned above;
- A baseline understanding of greenhouse gas accounting frameworks and methodologies;
- Strong theoretical background in Bayesian Statistics;
- Knowledge or experience working with crop, pedometrics, or process-based models;
- Experience coding in Python;
- Handling unstructured/imbalanced data.
What’s In It For You
- A unique opportunity to join and help shape a fast-growing tech scale up with the determination and ambitious mission to reverse climate change;
- A truly global environment where you can collaborate and socialise with diverse and passionate colleagues;
- Competitive compensation package and holidays;
- Centrally located modern office in Copenhagen or London and the option to work from home a couple days a week;
- Team events throughout the year;
- An exciting purpose-led culture and mission;
- Open communication and supportive feedback culture.
