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We are devoted to the Algorithmization of businesses. Our co-founders, Marta Díez-Fernández (LinkedIn, Instagram) and Sergio Álvarez-Teleña (LinkedIn, Instagram) are seasoned professionals of digital transformation with internationally recognized impact. 

"If we searched for a company that unifies skills along Advanced Finance, Science and Technology we wouldn't find many, but they would all be good. If we required those skills to be internationally recognized as tier one, we may find a handful but surely, they would all be special. If we further required those skills to blossom at their intersection, we would find SciTheWorld."

Along advanced finance, our co-founders have largely multiplied profits in different positions of trading, finally holding senior positions such as Global Head of Algorithmic Trading and Global Head of Data Science. Scientifically, their research is in use in the optimal quoting and hedging of several trading floors; and their human-bot convergence (©Augmented Machines/Systematic_me/ Avatar Calibration) has been recognized by the UK EPSRC due to its "Impact in the Digital Economy". Sergio is also Honorary Senior Research Associate at UCL; has been included by the UK's Digital Economy Network in the top 4 analytic researchers at UCL across all CDTs; and he is Honorary Member of Oxford University's Spanish Society. Technologically, they designed the platform that won the prize to the "Best Trading Platform" of the industry at the Banking Technology Awards, 2016. Furthermore, Sergio has been included in Choiseul's Top 100 list of future economic leaders.

All in all, SciTheWorld has wrapped up a world class judgement on algorithmic interaction. Whether this interaction is bot-human or, more interestingly, bot-bot. As a result, the company was launched in 2015 primarily to self-fund high risk-reward innovation through its founders' investments and, at the same time, to boost the algorithmic race on cybersecurity at a blue chip. They spent two years managing the difficult equilibrium of expanding their influence across banks and hedge funds while keeping full focus on the quality of their software. Such an effort gave rise to a technology that is not available in the industry and a network of clients that is already filtered.

Since 2018, they have boosted their reach by partnering up with a tier one strategic consultancy: Oliver Wyman. Therein, Sergio was already Senior Advisor for digital transformation (globally), advanced finance and cybersecurity (EMEA). Both companies want to explore this singular merge of skills so much that SciTheWorld's own office lies within their head quarters.


2015: KICK-OFF

Sergio and Marta's financial computing advances within a major digital bank grabs the attention of a cybersecurity manager who demands their help, ad-hoc. SciTheWorld is born.

They start exploring networks of thousands of servers from a defender ("Blue Team") and attacker perspective ("Red Team") with a trader mindset.

2016: SETTLE

Sergio's Thesis' algorithmic trading platform gets the prize to the Best of the Industry.

He joins SciTheWorld full-time until he finds a way for the platform to become a "Digital Brain".

He then becomes Global Head of Data Science at one of the most laureated digital banks.

2017: R&D

Sergio starts the platform that would beat the one already prized by the industry.

Sales are strategically postponed to grant a full focus on Financial Computing R&D - there is a natural bottleneck in its innovation (expertise across finance, tech and science) that can't be tackled by simply adding people.


Marta joins SciTheWorld full-time and underpins the platform to secure its major challenges are solved by the end of 2018.

Even though the industry shows actively interest on several CAT's modules, sales are still postponed to keep SciTheWorld's main resources focussed on the bottleneck. 

The partnership with Oliver Wyman becomes official. Commercial reach is exponentially boosted towards 2019.

CAT's agile skeleton is finally shaped. The bottleneck has passed.

Still zero external funding.


Given CAT's state, R&D going forward is easier to manage. It will be improved in terms of both features and autonomy.

The forthcoming challenge becomes the delivery of innovative projects (assets & services) across industries. Often,  completely new approaches will be offered - e.g. external audit of algorithms through Red Teams. Rough but niche and fun. 

External funding will for the first time be considered.

Looking fwd to seeing how it all starts settling.

2020: DEPTH

Once CAT has been used as the backbone of innovative A.I. assets & services, and further evolved during 2019, the R&D plan during 2020 follows a dive deep into trading's alpha.

CAT should be a game changer to its users. They should be able to onboard complexity into their algo trading at a much faster pace than those who did not pay enough attention to the bottleneck. Hence, the natural step at this stage - to simply help them take over.