Mastering Data in Commodities: In Conversation with Kpler's Director of Data Science

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We recently had the pleasure of speaking with Petar Todorov, Ph.D. , Director of Data Science at Kpler , a global trade intelligence platform. After completing a PhD in physics and transitioning to data science, Petar entered the maritime and commodities space.

Petar oversees the entire data science department at Kpler. His team works on advanced techniques such as computer vision on satellite and drone imagery, time-series modelling, geospatial predictive analytics on ship movements, and anomaly detection. Reflecting on what drives his passion for commodities, Petar said, “When I look at Kpler's data - the movement of all ships and commodities on water, which account for around 80% of global trade - I really feel like I’m in the engine room of the world economy.”

As data becomes more democratised in the commodities industry, what should be considered from a data science perspective?

The democratisation of data makes it challenging to consume and understand data at scale. Data quality is a big issue, and this is where data science algorithms kick in - particularly anomaly detection. When you spot an outlier, the key is determining whether it's a true outlier, indicative of a meaningful event, or just an error in the pipeline.?

At Kpler, we use different algorithms to address various data quality issues. For instance, when it comes to ship position data, we need to determine if an anomaly is due to an error in transmission, an issue on the ship’s end, or a deliberate manipulation of the automatic identification system (AIS) position. One of my teams focuses on risk and compliance, particularly detecting spoofing through pattern recognition of ship movements. In contrast, when we look at trades, say, commodities import data for a given country, we apply time series modelling to detect deviations from the norm.

So, as you can see, even within anomaly detection, we use entirely different techniques depending on the use case. Our main objective at Kpler is to always provide the highest quality data by leveraging the full range of data science tools.

What qualities do you prioritise when hiring for your teams?

I focus on whether candidates truly care about their models and what they produce. I want to know if they monitor their models in production, devise thorough tests to evaluate performance, and continuously iterate based on those results. How is the model performing on day zero? On day 100? How do they adjust and improve their models over time? It’s also crucial that a data scientist cares about how the end user consumes the data, as this helps them devise relevant monitoring metrics. In interviews, I specifically ask candidates how they handle their models hitting production. What happened? How did they manage that situation? For me, deployment isn’t the end of the process - it’s just the beginning of the development cycle. I’m looking for candidates who truly understand this.

Is previous experience in commodities necessary for joining your team?

I'm open to hiring people from diverse backgrounds because it brings fresh ideas and new perspectives. That said, knowledge of the commodities industry is beneficial. Individuals who have previously worked closely with physical and financial traders, will understand their use cases and can communicate this to the rest of the team.

There are also different subfields within data science that are more or less transferable to the commodities space. For example, professionals with previous experience in geospatial or mobility data can easily adapt to Kpler's use cases, as can those who have worked in anomaly or fraud-detection when addressing data quality issues. So, while prior experience in commodities is nice to have, I also see value in different perspectives.

What advice would you give to someone leading a data science team?

One of the first lessons for anyone entering the data science field is the importance of critical thinking. This includes admitting when there are negative results or when an experiment has failed and acting accordingly. It’s essential to maintain this mindset, even at the head of department level.

On a practical note, I believe every leader of a data science department should define and communicate a clear mission statement for their department. It’s important to clarify the purpose of the data science function within the organisation. Is it to facilitate innovative developments, or could engineers also handle data science tasks? Should data scientists and engineers operate separately, or can they share reporting lines? Every head of data science should reflect on these questions. For instance, at Kpler, I have a clear answer regarding our mission, but I encourage all leaders at my level to find their own.

What advice would you give leaders to secure internal buy-in for their data science teams?

This is an excellent question. To ensure data is recognised as an asset, we have to circle back to what I said about the importance of data quality and proactively detecting issues. Building confidence in the data is essential, along with employing data science forecasting and solid market analysis to make it more consumable. Standardisation, data quality, and a clear understanding of the data are key elements in this process.

What attracts talent to Kpler?

At Kpler we combine commodities and ship tracking expertise, making us a truly unique place to work. Occasionally, some team members develop a stronger interest in pure commodities trading and transition to our clients. That said, many who leave Kpler for trading desks or physical trading companies eventually return, and they liken that experience to switching from a Nokia 3310 back to a modern smartphone.

So, where we really do well is attracting talent who are interested in using cutting-edge technologies, which makes Kpler an attractive option for those eager to stay at the forefront of the industry.

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