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Data Science Engineering, AI & Machine Learning at Caspian One | For financial crime. In quantitative environments. Across all financial and banking technologies; trading, retail, investments, and FinTech start-ups.

Our clients demands for data science capabilities are exponentially expanding, as the benefits of well-managed and visualised data become more and more prominent.

Leaders at financial institutions are ever increasingly looking to utilise data science across their businesses, to vastly improve how they set direction and strategically plan ahead.

The advancement of languages including Python, has enabled an explosion of new data practices - with huge demand from clients for people, and teams, that can accurately and effectively deliver data-first projects.

At Caspian One the three core areas of data science we are aligned with include:-

Financial Crime | Providing teams that can meet outcomes set for projects designed to spot potential criminal patterns; analysing customer behaviours, trading behaviours and similar - quickly identifying money laundering activities or other illegal events.

Quantitative | Many of the new libraries in quant environments are written in Python. We're helping clients access diverse teams of Data Scientists and Engineers with the capacity to build new research systems and more.

General technologies | Given the broad potential for greater data management, our partners are utilised in a huge variety of business locations - working with data to improve insights, analytics, strategies and infrastructures. This can vary widely - from data visualisation, to data mining, manipulation, modelling and so-on.

Like most areas of finance, this competency is also under pressure from issues impacting the UK markets - and the niche nature of many data-focused projects can make access to credible people highly competitive.

  • Data acquisition, data extraction
  • Data reporting, data visualisation, Business intelligence
  • Data manipulation, data mining, data modelling, statistical analysis
  • Predictive analysis, regression, text mining, qualitative analysis
  • Machine learning, artificial intelligence, robotics, neural networks
  • Python, R



          Market People Types:
          Data Analyst

          Data Analysts bridge the gap between Data Scientists and Business Analysts. They are provided with the questions that need answering from an organisation - and then organise and analyse data to find results which align with high-level business strategy. Data Analysts are responsible for translating technical analysis to qualitative action items; effectively communicating their findings to diverse stakeholders.

          Skills needed:

          Programming (SAS, R, Python), statistical and mathematical capabilities, data wrangling, data visualisation.




          Market People Types:
          Machine Learning Engineer

          Designing and developing machine learning (ML) and deep learning systems, running ML tests and experiments whilst implementing appropriate ML algorithms. ML Engineer responsibilities include creating ML models and retraining systems.

          Skills needed:

          Programming languages (Python, Java, R), data modelling, a deep knowledge of statistics and algorithms, familiarity with Machine learning frameworks.



          Market People Types:
          Quantitative Developer

          A Quantitative Developer (QD) is a computer programmer and software engineer who writes code and develops trading infrastructure for Investment Banks and Hedge Funds. As a QD, your duties include creating and testing financial models and forecasts, validating and documenting the performance of financial models, analysing performance results - and reporting on the data to traders, financial engineers and IT support.


          Skills needed:

          Programming languages (Python, Java, C++), data engineering, data manipulation and a strong mathematical/computer science focused background.