At a Glance
- Tasks: Build models and datasets for raw materials, focusing on cost and carbon impact.
- Company: Join a pioneering company transforming the raw materials economy with innovative software.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on impactful projects and career advancement.
- Why this job: Make a real difference in sustainability by modelling upstream material systems.
- Qualifications: Expertise in raw-material systems and strong data science skills in Python.
The predicted salary is between 63000 - 77000 £ per year.
Everything physical is made from raw materials: polymers, chemicals, metals and minerals, a $16T economy that software has barely modelled. Atmospheric AI computes what those materials should cost, what carbon they carry and where their supply is exposed, from the physics of how they're made, so manufacturers can decide and negotiate from ground truth.
The problem: Before a polymer or a metal reaches a converter, it has been extracted, refined and transformed somewhere specific, with technology that sets both its cost and its carbon. A buyer asking why two suppliers' footprints differ needs that upstream picture by location and technology, with the uncertainty stated. You'll build it as validated data and Python models that run inside the product.
What you'll own:
- New feedstock categories: Map pathways from extraction through refining into industrial transformation, with the assets, technologies and system boundaries involved, to a design ready to build.
- The upstream model layer: Datasets and models for where materials are produced, how production varies by location and technology, and the carbon intensity that goes with it.
- License, adapt or build: Judge materiality, coverage and resolution before choosing, and build new methods selectively where existing sources fall short.
- Upstream data products: Evaluate, ingest and reconcile open and commercial datasets, resolve assets across conflicting sources, and ship documented, versioned outputs.
- Validation and provenance: External benchmarks, quantified uncertainty, and each important output carrying its source, vintage, allocation choices and known limits.
- Upstream authority: The person the team asks whether a pathway, production estimate or carbon result is physically and commercially plausible.
What we're looking for:
- Deep upstream expertise in at least one raw-material system: oil and gas, mining and metals, forestry, agriculture or land use. Deep enough to challenge an answer that is statistically plausible and physically wrong.
- The ability to transfer it: You learn unfamiliar commodity systems fast and build a defensible model of them.
- Strong GHG and LCA knowledge: System boundaries, allocation, land-use change, methane and other non-CO2 emissions, and where common datasets fall short.
- Applied data science in Python, documented well enough to be reviewed and productionised. You've owned an important dataset or model that others relied on: its methodology, quality, evaluation and maintenance.
Nice signals: geospatial, raster or satellite-derived modelling; familiarity with OPGEE, PRELIM, OCI+, ClimateTRACE, GEM or Trase; more than one commodity system; production software experience; a Master's or PhD in a relevant field.
Stack: Python, FastAPI, Postgres, Dagster, AWS. A signal, never a requirement.
Upstream Domain Scientist in London employer: Atmospheric AI
Atmospheric AI provides a collaborative work environment where employees can engage in meaningful projects that address critical environmental challenges. The company emphasizes professional growth and offers opportunities to work with innovative technologies in the field of data science. Employees are part of a team that contributes to impactful solutions in the materials economy.
StudySmarter Expert Advice🤫
We think this is how you could land Upstream Domain Scientist in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Atmospheric AI!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Upstream Domain Scientist at Atmospheric AI.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Atmospheric AI.
✨Apply Directly through Our Website
When you find a suitable opening like Upstream Domain Scientist at Atmospheric AI, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Upstream Domain Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Atmospheric AI, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Atmospheric AI. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Atmospheric AI
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Atmospheric AI!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
