At a Glance
- Tasks: Build models to estimate commodity prices and support procurement negotiations.
- Company: Cutting-edge AI firm transforming the raw materials market.
- Benefits: Competitive salary, flexible working hours, and opportunities for growth.
- Other info: Dynamic work environment with a focus on innovation and collaboration.
- Why this job: Join a team revolutionising pricing in a $16T industry with real-world impact.
- Qualifications: Experience in quantitative modelling and production-quality Python.
The predicted salary is between 59400 - 72600 £ 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
Benchmarks are public. The price a converter in north-west Europe paid a Middle East supplier for a film grade isn't. Procurement teams need that number, with a range they can defend, before they sit down with the supplier. You'll build the models that estimate it.
What you'll own
- Transaction-price inference. Estimate what a buyer at one facility likely paid a supplier at another, given the grade, the geography, the market structure and the likely contract.
- Pricing mechanics. Represent PRA benchmarks, formula and cost-plus contracts, grade differentials, regional premia, netbacks and captive transfers.
- Should-pay ranges. Turn estimates into explainable ranges customers take into supplier negotiations, with the evidence and uncertainty on show.
- Price signals. Source, version and maintain the benchmark, feedstock and utility prices used across the platform.
- Validation. Test estimates against withheld prices, report error and bias, and say plainly where the model can't support a claim.
- Production. Ship the models as tested Python modules, and shape how we add forecasting as the pricing layer matures.
What we're looking for
- You know how opaque physical commodities are priced and contracted, beyond how their futures trade. We can teach our stack; we can't teach years in these markets.
- Quantitative modelling under sparse, noisy data, and the judgement to know when more complexity stops paying.
- Production-quality Python. Interfaces, tests, review, reproducibility.
- Commercial sense. You can explain to a procurement lead why the benchmark isn't the price they pay, and give a useful range when the market won't support a precise one.
Nice signals: more than one commodity complex; Argus, ICIS, Platts, Fastmarkets or CRU data; should-cost or negotiation work; price forecasting; a quantitative Master's or PhD. Experience only in liquid financial instruments is unlikely to be enough.
Stack: Python, FastAPI, Postgres, Dagster, AWS. A signal, never a requirement.
The take-home: estimate the fair delivered price of a named polymer grade between two regions, with a range, the evidence behind it, how you'd test it and what would change it.
Trading & Pricing 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 Trading & Pricing 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 Trading & Pricing 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 Trading & Pricing 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 Trading & Pricing 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.
