In an increasingly digitised world, instances of fraud and electronic crime are becoming more and more common. Investigative Analytics involves the analysis of data to support forensic investigations. Much of our work requires us to analyse vast quantities of data, using cutting edge technology and advanced statistical techniques to extract meaningful insights for our worldwide clients.
Because we operate in an environment where speed of response is often critical to our clients, we are constantly innovating and drawing on the very latest technologies.
Join us and you could be looking for hidden patterns within banks trading systems, looking for potential evidence of market abuse or incidents of rogue trading. In addition, you could be searching for evidence of fraud and abuse within a company’s finance system or helping to identify illegal activity within millions of regular transactions.
You may also be researching new tools and technologies and developing new propositions to take to our clients.
The very nature of what we do means that many of our clients have an international reach, and no two jobs, or indeed days, are the same.
So, if you are naturally inquisitive, have an analytical mind and enjoy solving problems in a rigorous and methodological manner, we’d be interested in talking to you.
Your responsibilities will include but may not be limited to
In-depth understanding of machine learning concepts across all areas, in particular; supervised, unsupervised and reinforcement learning.
Writing Python / R code to create, tune and validate models.
Experience optimising code and infrastructure for machine learning performance.
Consulting with clients on business issues, often explaining complex technical concepts to non-technical people.
Creation of novel visualisations and explanations to demonstrate our work.
Researching new products, tools and techniques to assist with advancing our machine learning and data science capability.
To be considered you must be able to demonstrate experience working in a data science role, covering multiple of the following disciplines
Data transformation and modelling (e.g. pandas and scikit-learn in Python);
Data storage and querying (e.g. SQL);
Understanding of common data quality issues and they effect they have on machine learning models;
Data cleansing and manipulation for machine learning (e.g. feature engineering);
Dimensionality reduction techniques (e.g. principal component analysis);
Hyperparameter optimization approaches for a range of models;
Clustering and/or text mining concepts and techniques (e.g. LSI);
Experience with financial / general ledger data;
Knowledge of the current data science software platforms.
To be successful in this role you will need to demonstrate the following
The ability to come up with creative solutions to complex problems;
Exceptional analytical and technical aptitude;
Exceptional attention to detail;
The ability to manage time, prioritise tasks and work under tight deadlines;
The ability to work independently with little supervision, but integrate well into teams;
Concise and clear communication when presenting and explaining results and findings.
Job ID: 2827125