Education / Experience and Skill Requirement
4-6 years of relevant experience in :
Experience working in quantitative modelling development projects.
Experience in building ETL processes, data pipelines using standard Python Anaconda libraries.
Strong proficiency with programming in Python. Python PEP8 coding best practices is required. Proficient with Python list comprehensions and optimization.
Strong OOP (Object Oriented Programming) concepts, and strong experience of good modular design in Python.
Strong pre-processing, machine learning, statistical inferential analytics, exploratory data analysis experience.
Strong data integration experience. Experience with building data controls.
Experience in working with acquiring Financial data sources (Factset, Bloomberg, Reuters, Dealogic) is desired
Strong communicator (verbally and written). Independently manage daily client communication, especially over calls
Additionally, desired skills
R programming (Modelling, data pre-processing)
NLP processing, entity pattern matching experience
As part of the Data Science practice, you will be involved with projects which generate data-driven predictions and insights by designing, implementing and testing data science based solutions.
Key responsibilities include :
Design and develop quantitative solutions for the data science group of one of the largest investment banks
Have a strong understanding of the domain knowledge
Collaborate with the subject matter domain experts, data engineers and data scientists to ensure data quality, accuracy and completeness.
Collaborate with data engineers to build data pipelines and ETL frameworks using standard Python Anaconda libraries.
Collaborate with data scientists and subject matter domain experts to perform EDA, engineer features and build quantitative models.
Be able to perform data analysis using Jupyter Notebook and Microsoft Excel.
Be proactive and independent and be able to deliver projects with minimum inputs from client stakeholders.
Evaluate and ensure quality of deliverables within project timelines.
Ensure effective, efficient and continuous communication (written and verbally) with global stakeholders.