Data science

Data Science Engineering Capabilities. For financial crime, in quantitative environments - across all financial and banking technologies; trading, retail, investments, FinTechs and start-ups.

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Our client’s 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.

Data Scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills, as well as the ability to mine, clean and present data.

Businesses use Data Scientists to source, manage and analyse large amounts of unstructured data. Results are synthesised and communicated to key stakeholders to drive strategic decision-making.

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 global markets - and the niche nature of many data-focused projects can make access to credible people highly competitive.

 

 Data Science Expertise:

  • 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

Business Domain Experience:

  • Quantitative Finance

  • Systematic Trading

  • High-Frequency Trading

  • Front Office

  • Financial Crime

  • Fraud Detection

  • Forecasting

  • Marketing

Client Type Experience:

  • Hedge Funds

  • Investment Banks

  • Retail Banking

  • FinTech Firms

  • Start-ups

  • Tech Companies

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.

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.

Data Engineer - Data Engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management and optimisation of data pipelines and infrastructure - to transform and transfer data to Data Scientists for querying. Skills needed: Programming languages (Python, Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop).

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.