First DS at small oil trading firm – what ML actually helps physical/paper desks?

Just joined a small commodity trading firm as the first Data Scientist/Analyst.

Background: ~7+ years in ML (mainly NLP, but comfortable with time series / forecasting & deployment). The firm is mostly physical oil trading, but my manager wants to grow paper/spec trading and use Data to support it 

We have vendor that already provides a lot of analytics. There are no clear internal requirements – my mandate is basically: look at the data, find useful use cases, and build something that will be useful

I’m much stronger in ML/models than BI. I can build dashboards, but would rather focus on models / tools that support trading/business decisions. 

Questions for people who’ve seen this work in practice:

  1. What in-house ML/analytics have you seen that genuinely helped physical or paper oil traders?
  2. Where would you start as the first DS on such a desk, given vendors already cover generic analytics?
  3. What are the career prospects for someone entering oil trading from the DS side? 
23 Comments
 

Based on the most helpful WSO content and insights from similar discussions, here’s a breakdown of how you can approach your role and make an impact as the first Data Scientist at a small oil trading firm:

1. ML/Analytics That Genuinely Help Physical or Paper Oil Traders

  • Forecasting Supply/Demand Imbalances: Use time series models to predict supply/demand shifts based on refinery turnarounds (TARs), weather patterns, geopolitical events, or shipping data. This can help traders anticipate price movements and optimize logistics.
  • Price Arbitrage Opportunities: Build models to identify price discrepancies across regions or between physical and paper markets. This could involve analyzing futures curves, spreads, and basis differentials.
  • Sentiment Analysis: Apply NLP to news, social media, or earnings call transcripts to gauge market sentiment or detect early signals of market-moving events.
  • Inventory and Storage Optimization: Predict storage levels and costs, helping traders decide when to store or release oil based on contango/backwardation in the market.
  • Trade Execution Optimization: Develop algorithms to optimize trade execution by analyzing historical trade data, order book dynamics, and market liquidity.
  • Risk Management Tools: Build models to assess and hedge risks, such as VaR (Value at Risk) or stress testing for extreme market scenarios.

2. Where to Start as the First Data Scientist

  • Understand the Business Context: Spend time with traders and operations teams to understand their workflows, pain points, and decision-making processes. This will help you identify gaps that ML can fill.
  • Leverage Existing Vendor Data: Since vendors already provide generic analytics, focus on creating proprietary tools/models that give your firm a competitive edge. For example:
    • Combine vendor data with internal trade data to create unique insights.
    • Build predictive models tailored to your firm’s specific trading strategies.
  • Quick Wins: Start with low-hanging fruit that demonstrates value quickly. For instance:
    • Automate repetitive tasks like data cleaning or report generation.
    • Build simple forecasting models for key metrics (e.g., crude runs, refinery margins).
  • Data Infrastructure: Ensure you have the right infrastructure for data collection, storage, and processing. This might involve setting up pipelines for real-time data ingestion or creating a centralized data repository.
  • Collaborate with Traders: Build trust by involving traders in the development process. Show them how your models/tools can directly improve their P&L.

3. Career Prospects for a Data Scientist in Oil Trading

  • Specialized Expertise: Gaining domain knowledge in oil trading (physical and paper) will make you highly valuable. Few data scientists have deep expertise in commodities trading, so this niche can set you apart.
  • Transition to Trading: Over time, you could transition into a trading role if you develop a strong understanding of the markets and trading strategies.
  • Leadership Opportunities: As the first DS, you’re in a position to shape the firm’s data strategy. This could lead to leadership roles as the team grows.
  • Broader Commodities/Finance Roles: Your experience in oil trading analytics can open doors to other commodities or financial markets, where similar skills are in demand.

Additional Tips

  • Focus on Explainability: Traders often prefer simple, interpretable models over black-box algorithms. Ensure your models are easy to understand and actionable.
  • Stay Agile: In a small firm, priorities can shift quickly. Be prepared to pivot and adapt your projects based on business needs.
  • Network and Learn: Engage with other data scientists in the commodities space to share ideas and stay updated on best practices.

By starting with impactful, trader-focused tools and building trust with the desk, you can establish yourself as an indispensable part of the team. Good luck, and welcome to the world of oil trading!

Sources: AI in fundamental investing, Is there a future for non-quant hedge funds?, Machine Learning in fundamental HF?, Physical Energy Trading And Logistics, https://www.wallstreetoasis.com/forum/hedge-fund/machine-learning-taking-over-hf-research-analyst-roles-in-near-future?customgpt=1

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 

Which efforts have failed in particular? Maybe the have before LLM adoption, now it should be faster to provide some internal ML product thanks to faster iteration.

 

OLS is the only useful "model." I am not joking. You will not get traction on trading ideas with anything more complex and even the OLS should not have more than 2-3 variables in it which themselves are easily explainable and pass the smell test on having an impact on your target. Transformations on the data will also make people hesitant. Simple variables that be explained with a simple scatter plot is the only way. If you manage to print a bunch of money doing that then you can think about more complex models. Odds are most people at a physical oil shop don't know what standard deviation really is. They will nod and make you think they do but they don't.

 

Why did you join without a clearly defined mandate? In my experience DS initiatives tend to fail when the roadmap and expectations are not clearly defined and aligned prior?

Anything you build doesn't have to be tradable, it can be things that make traders lives easier. I would spend a significant amount of time trying to understand what the traders do, what data they interact with and why etc to get an understanding of what might be useful and generally build some intuition

 
Most Helpful

Sorry, but agree that you will probably quit soon.  

You are the first data hire in a space that is difficult to systematize at a seemingly small company which is data illiterate.  And there are way bigger institutions before you which have tried a lot harder with a lot more money, tools, data scientists and also more experienced traders to guide them, and mostly to no avail. 

They hired you as a low downside gamble.  Traders will secretly think that the things they ask you to check are probably a dead end, but they will do it anyways because you are going to be the one doing the work, wasting time, getting frustrated, not them.  Prepare to hear the words “can we look into” something that sounds conceptually plausible that no one can seem to get to work.  

Your most achievable deliverables will be things that are mechanically functional.  Can you download data on a schedule, put it into a dashboard, notify people if there are big changes.  It’s a living but it’s not glamorous. 

 

oil_quant

Sorry, but agree that you will probably quit soon.  

You are the first data hire in a space that is difficult to systematize at a seemingly small company which is data illiterate.  And there are way bigger institutions before you which have tried a lot harder with a lot more money, tools, data scientists and also more experienced traders to guide them, and mostly to no avail. 

They hired you as a low downside gamble.  Traders will secretly think that the things they ask you to check are probably a dead end, but they will do it anyways because you are going to be the one doing the work, wasting time, getting frustrated, not them.  Prepare to hear the words “can we look into” something that sounds conceptually plausible that no one can seem to get to work.  

Your most achievable deliverables will be things that are mechanically functional.  Can you download data on a schedule, put it into a dashboard, notify people if there are big changes.  It’s a living but it’s not glamorous. 

 They indeed are data illiterate. But I can try to bring some data culture into the firm, make them data-literate, can't I? 

So what should I do? Do you know any low-hanging ML fruits in oil industry? 

Are there any career opportunities for me outside this firm, such as moving to a bigger trading company?

 

Ya I think the low hanging fruit is just going to be operational in nature- getting updated data, showing it. That type of thing.

There’s not been much progress in ML for any type of oil trading that I’m aware of.  

There are very simple regression type things that you can do- what is this price does it cause this pipe to flow more or less.  But nothing super fancy, and as mentioned before, fanciness might result in less buy in.  

And if they gave you a “vendor” that “does all their analytics” like energy aspects, I’m not sure mining that is going to result in a ton.  And is also a red flag to me- like if they don’t even do their analytics I’m not sure they are anywhere close on the data culture front to being productive with anything more advanced.

 

I would say build your own CTA models, momentum based & try trade if off based there. Agree with others comments, seems you’re a low investment/high reward profile, but it’s high reward for you as well personally as a career. I wouldn’t try to jump immediately since they let you tinker around & trade. I think a lot of guys would be willing to do what you’re doing in order to get to a trading seat. I also think the trading seats this year are lesser than before & expected to stay the same as it’s been a challenging year for oil as a whole

Vortexa/Kpler have other functions you may like

Energy Aspects has a quant subscription you may be keen on enrolling

As for seasonality, you can look into RBOB & HO & ICE gasoil… though it’s not going to be easy & it’s incredible volatile, you have been warned. Look out for futures positioning for starters & see if you can derive anything useful

As for Brent/TI, it’s highly macro right now & maybe an OPEC discount against physical at the moment, so you’ll need to pull all macro variables & see what suits best

 

Other thing is… only you will know… is your company downsizing physical.

that may also be the case why they decided to hire you as a gambit, but potentially as a spec replacement to another phy trader. As mentioned, this year physical for some of streams, are just horrible & every 3 months we see someone exiting or downsizing. so back to original equation, low risk gambit, but high reward for the company & yourself if you figure it out ;)

 

If the company is physical and been around a long time - here is a use case - lots of times various grades and not published well but rather send out daily via broker quotes in PDF, excel sheets etc. scrape this data using NLP and build a “exotic” grade pricing data base and then try to analyze how various grades trade relative to each others. Then the next step would be to try and build a refinery model that based on grades and freight you can optimize what kind of slate it should be running …you can move on from here

 

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