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Wanted: Data scientists for jobs in commodity trading

Alessandro Sanos, CAIA, SCR
Alessandro Sanos, CAIA, SCR
Global Director Sales Strategy & Execution, Commodities, Refinitiv

The prized jobs in commodity trading are now being filled by data scientists as the industry’s digital transformation gathers pace. Alessandro Sanos, CAIA, Market Development Manager, Commodities, explains the value of candidates with capabilities in programming, big data concepts or data visualization.

  1. Where relationships were once the main asset, now it’s the ability to programmatically collect, store, analyze and monetize data that is driving jobs in commodity trading.
  2. Commodity houses are finding they can use the same data science platform to underpin trading decisions in apparently diverse markets.
  3. The industry is starting to compete with financial institutions in terms of what it is willing to pay data science candidates for jobs in commodity trading markets.

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The speed of digital transformation in the commodities market has been accelerating at an unprecedented pace. Increased regulation and the democratization of data has brought more transparency in an opaque market where traders with proprietary information used to have an edge.

One of the recurring themes that emerge from market participants is that there is an increased focus on leveraging technology to extract alpha from data to compensate for thinner margins.

Jakob Bloch, CEO of Commodity, addresses how the skill requirements are changing for jobs in commodity trading as data scientists have started to replace the traditional trader.

Jakob Bloch, CEO of Commodity

Q. As a boutique recruiter active in commodity markets for 40 years, how is the changing skills landscape altering the commodities trading workforce?

Jakob Bloch: What’s bigger than a change? A metamorphosis, maybe? Whatever it’s called, it’s happening right now in the commodities trade and it’s being driven by the realization that more people must have a fresh set of skills in order to excel.

Where relationships were once the main asset to have in a trading environment, now it’s the ability to programmatically collect, store, analyze and monetize data that is king.

I know the word data can send readers to sleep, but let me assure them we are talking about something that is real, immediate and long-lasting. It is also coming in at an amazing speed.

Every year at Commodity we track thousands of people moves and jobs in commodity trading internationally. In 2018, we saw a significant number of people hired as data scientists in one form or another — yet this is a role that had not figured at all in our statistics as recently as 2016.

Of course, it means a major transformation in the skills landscape, as your question suggests. Candidates who are highly capable in programming, in big data concepts and in data visualization will be highly sought after and will carry a premium over the traditional trader of the past decade.

But you are also right to ask about the impact on the workforce. What we see in particular is how its structure is being transformed.

In the past, the trader-to-analyst ratio was skewed heavily in the favor of traders. From now on, expect a team to consist of multiple research analysts and fewer execution-focused traders. In other words it will be a set-up more akin to a hedge fund than to a big commodity trading house.

Q. From a headhunter perspective, how fast are the talent demographics evolving to match the skill requirements?

Jakob Bloch: Inevitably, demand outstrips supply in the data handling arena at present and for sure that will continue. After all, it takes time for enough incoming people to acquire the required skill set and then decide to enter the commodities business, even before they become fully up to speed in market mechanics.

However, don’t imagine that it’s only young graduates who are at the forefront of this skills search. We also see experienced traders and analysts embracing data science and re-defining their trading approach, as well as showing a willingness to learn programming code.

Hats off to them when they succeed. I have great respect for anyone who manages to do it. The big plus in their favor will be, of course, that they already know the trading business inside out. They will be in a perfect position to interpret and explain the data science results to the traders who work with them.

It’s a positive message from everyone concerned. In a poll earlier this year, the resounding response from the trading community was that this skills upheaval is not going away and therefore everyone needs to adapt in order to continue flourishing professionally.

Q. Power and gas was the first commodities market to embrace data science. Is there now a fundamental shift across the whole commodities industry?

Jakob Bloch: Yes, power and gas were the first, because they had vast swathes of data readily available. In the case of power, another factor was inelastic supply, which allowed trading businesses to model the market with impressive accuracy. But we are already looking well beyond those horizons. This digital transformation is sweeping through the entirety of the commodities markets.

Make no mistake, the other segments are catching up. They’ve been led by the wider energy commodities spectrum; now even the traditionally opaque agricultural markets are following the data science direction. It’s all been brought about by the dramatic increase in the amount of information in the public domain, resulting from regulatory changes along with increased sophistication, improved liquidity, and the proliferation of exchanges.

Q. Is there a difference in how financial institutions have been attracting talent with a data science background? Are commodity companies behind?

Jakob Bloch: At the moment there’s no denying that the hedge funds are still far ahead of the physical commodity houses in attracting this type of talent. Banks and other financial bodies come somewhere between. Hedge funds have always sought their edge in research and analytics so they see the data science upsurge more as an evolution than a revolution.

Therefore, I’d have to agree that commodity companies are lagging by comparison. They were more used to benefitting from opacity, where their market share provided a significant competitive edge. It has handed them a legacy issue of inefficient data collection and warehousing.

To catch up with financial institutions, therefore, they must first address an urgent need to organize their proprietary data in terms of standardizing, collecting and analyzing before they can even consider monetizing it.

They see clearly what’s needed, though. Notice that a first step of achieving data standardization has been announced for the new initiative started by five of the world’s largest grain traders to digitize ag shipping transactions.

With Cofco International now on board, joining the launch team of Bunge, Cargill, ADM and LDC, the chances have grown even stronger that this standardization will become industry wide.

It’s been interesting to observe them suddenly discovering the merits of collaboration where before they had probably seen each other only as competitors. Perhaps the collaborative spirit shown by many new businesses in the trading sphere has spread to the big name operators, to the benefit of the whole industry.

Although it’s true the hedge funds have been the early talent magnets in this whole data area, I firmly believe that data science has the potential to provide more opportunities to trading houses than to financial institutions in the longer run.

On top of the increase in publicly available data on the commodity markets, physical trading companies typically do have valuable proprietary data to give an additional benefit to their business.

They also have more chances to use data science with a move toward predictive rather than reactive analysis, whether this be in logistics or for increased efficiency in the hedging of their physical flows.

Commodity houses which trade in multiple commodities are already finding a crossover, meaning they can use the same data science platform to underpin trading decisions in apparently diverse markets. Even better, lessons learned in one arm of the business can be applied in other arms.

The talent wins again in this case. In a compensation survey run with 200 commodity professionals in 2018, we could see that the trading houses were starting to seriously compete with the financial institutions in terms of what they were willing to pay for a chosen candidate.

In your view, how can Refinitiv help address the talent needs of commodities companies?

Jakob Bloch: Refinitiv can play an important part, not least in ensuring that commodities markets remain an exciting and enticing employment prospect for the new generation of talented people.

They do have an amazing choice, after all. From our experience of helping commodity trading houses to find data-focused talent, it is clear that the diversity of competitors all seeking the same skills has become much wider.

First, then, as a trusted information resource you can alert them to the rapid pace of change and the development of new opportunities within the market. More fundamentally, by continuing to provide high-level data you will certainly excite the new data scientist who lurks today inside every trader and analyst!