If you want to work in data science in finance, there are a few things you should probably know: firstly, that advantages are increasingly conferred in real time by huge unstructured data sets derived from social media, and secondly that while the building blocks of data strategy (data storage and administration) are increasingly, commoditized there are still interesting roles for data scientists at the top of the “data stack.”

“Data engineering teams are really shifting their focus from low level database storage and administration and are instead focusing much more on the high value-add part of the data chain,” said Tom Taylor, head of alpha technology at fund Man Numeric, speaking at this week’s AI & Data Science in Trading conference. 

Alternative data like credit card transactions and brand sentiment tracking has now become the norm in finance, said Taylor, and there’s so much data around that to be successful funds don’t just need a team of researchers analyzing its meaning, but an “industrial scale data onboarding and data science capability.”

Some funds, like Two Sigma, have outsourced this process of cleaning data so that it can be onboarded into their systems easily, and companies like Crux Informatics (used by Two Sigma) are emerging as specialists in so-called ‘data wrangling’ – ingesting, cleaning and structuring data sets. 

Important as it is, however, data wrangling isn’t where the most appealing data jobs are. If you want to work in some of the most interesting and highest value-adding data jobs in finance, Taylor suggests you should position yourself towards the top of the “data stack.”

At Man Numeric, Taylor said the data stack looks like this. The highest value positions are at the top, the lowest are at the bottom.

‘Data stack,’ Man Numeric:

While you might be able to get a data job in finance if you’re an expert in SQL, kafka or kubernetes, therefore, you won’t get the best data jobs just by knowing about data storage and computation packages. Nor will knowing about machine learning or open source Python libraries (Man is a Python house) be the deciding factors. – The best data science jobs on the buy-side now go to people who can make data available to the rest of the organization, said Taylor.

The future is about increasing the “velocity of data” in firms, and this means enabling “self-service data,” he said. While data ingestion and storage are important, they’re at the commoditized end of the stack. The real focus is now on allowing, “users from across the organization to manipulate and analyze data.” 

This means that the new most valuable data scientists are those who can build custom dashboards or visualizations that allow colleagues to access data directly. – Data science teams have an “empowering and enabling role.” It’s not just data science teams who will do data science. Taylor said that data fluency across organizations is increasing, both as existing staff add to their skills and as new data-confident graduates are hired. 

If you want a long term, remunerative career in data science in finance, you therefore need to be pitching yourself at the top of the chart above. “If you can build self-service tools, you will be ready for the next few years,” Taylor said.

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Artificial intelligence has long been a prevalent talking point in the industry, and as consumers and dealers demand more digital capabilities, lenders have set their sights on implementing technology to enable faster, more efficient processes.

In this special preview session of the Auto Finance Innovation Summit, sponsored by Alfa, Blaise Thomson, director of Alfa IQ, discusses the practical applications for artificial intelligence and machine learning in auto finance.

For more content like this, register for the Auto Finance Innovation Summit, taking place March 16-17. The event will cover topics such as compliance in a digital-first era, finding success in startup partnerships, and scaling digital initiatives for improved customer experience. Find the full agenda and more information at AutoFinanceInnovation.com.

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If you’re looking for a data science role in financial services, and are expecting to earn an enormous salary and bonus, you may well be disappointed. Data science roles in financial services come in a variety of forms and some pay a lot better than others. 

A new salary and bonus survey from recruitment firm Octavius Finance highlights the discrepancies. Not only are some roles more lucrative, but some types of employer are more generous too.

Octavius Finance splits data science jobs into five categories, defined as follows:

• Data strategists and relationship managers: People who researching new data sources and manage vendor relationships.
• Data analysts: People who engage in exploratory data analysis to make data consumable.
• Data engineers: People who clean and organize data and build data pipelines
• Data scientists: People who harvest Alpha, combine alpha signals, engage in systematic backtesting and machine learning.  
• Quant researchers: People who building systematic trading models, strategies and trading signals

As the pay charts below show, data scientists and quant researchers are by far the best paid in London. Moreover, hedge funds are far more lucrative places to work as a data scientist than investment banks. 

Quant researcher salaries and bonuses are shown in the final chart on their own. Octavius director Natalie Basiratpour says hedge fund bonuses aren’t included because they vary widely depending upon factors including the size of the team, assets under management and the performance of the signal. 

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One of the companies leading the way is FiO, an innovative new “middleware” ecosystem that has partnered with IBM and is set to help businesses take advantage of the latest blockchain technology.

Following Blockchain 1.0 (digital assets) and 2.0 (smart contracts), the approaching Blockchain 3.0 era promises to create compliant independent financial markets and economies that will force businesses to join in order to stay competitive.

Yet, constantly evolving technology and exorbitant research and development (R&D) costs make blockchain technology still prohibitively unaffordable for small-to-medium enterprises (SMEs). FiO aims to decrease the barriers to entry and help SMEs integrate blockchain and big data-driven targeted marketing into their businesses. FiO is in its final stages of development and will be completed by the end of October; furthermore, FiO will be online by the end of 2019.

Blockchain as a Service (BaaS)

Blockchain technology is decentralise, incorruptible, and immutable by design in order to facilitate secure data storage.

FiO, an official IBM Hyperledger partner, builds on this with a seamless plug-and-play solution that uses a simple API protocol to effortlessly integrate with companies’ existing software.

FiO’s MCC (multi-cross-chain) solution acts as a flexible layer that allows clients to keep their data on either a private or public chain (or both), while its tokenization service foils traditional provenance fraud and theft by transforming ownership certificates into immutable blockchain records.

AI-enhanced Targeted Marketing

Additionally, FiO clients can also easily collect anonymity, GDPR-compliant user data. FiO uses IBM Hyperledger Indy’s Decentralised Identification (DID) to enhance blockchain big data with cutting-edge AI applications.

FiO’s tech-driven targeted marketing platform will create a sharing economy set to mutually benefit both businesses and their customers. The concept is simple — participants are incentivised to provide data, yet retain full control over their privacy. By compensating users who share data via a subscription-based model, it allows businesses to optimise their targeted marketing strategy.

In addition to partnerships with IBM and leading R&D partners, FiO also has the financial backing of Forbes Asia and Infinity Leadway, an Israel-based venture capital fund. FiO is currently rapidly expanding in Asia, with offices in Taiwan, Hong Kong, mainland China, Japan, and Singapore.

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