Data Scientist


My Client is a global provider of financial markets data and infrastructure and is quite keen in looking for Sr Data Scientist with Deep learning / Machine learning experiences along with experience in programming language with Python and R.

Idea candidate should be coming from Financial services who will be involved in Labs which are involved in a large range of projects leveraging a multitude of different technologies, so a successful candidate is unlikely to be assigned to a single product or service.

What you will be doing

  • Creating hypotheses about ways to change the financial industry through new uses of data
  • Building prototypes and MVPs to validate ideas by practically applying data science
  • Working with large, diverse data sets using big data and public cloud technology
  • Defining, organizing and running projects across multiple locations and time zones
  • Translating end user needs to requirements using design thinking methodologies
  • Build minimal viable products utilising data science techniques directly for our customers
  • Create compelling proposals with technologists and business to drive innovation from conception to production with appropriate success metrics
  • Lead projects, removing obstacles and taking ownership to find creative solutions
  • Build domain expertise in financial market content

What you bring

  • 3+ years industry experience working in a data science role, such as statistics, machine learning, deep learning, quantitative financial analysis, data engineering or natural language processing
  • Experience producing and rapidly delivering minimum viable products, results focused with ability to prioritize the most impactful deliverables
  • Extensive experience using Python or R
  • Experience with Relational, NoSQL & Graph databases, such as PostgreSQL, MongoDB, Elasticsearch or Neo4J
  • Experience in building and deploying unsupervised, semi-supervised, and supervised models on large datasets
  • Ability to track down complex data quality and data integration issues, evaluate different algorithmic approaches, and analyse data to solve problems. Curiosity about the details of datasets is essential