5-8 years of experience with minimum Masters/Phd in a quantitative field such as applied math, statistics, engineering, physics, accounting, finance, economics, econometrics, computer sciences, or business/social and behavioral sciences with a quantitative emphasis.
Strong mathematical, statistical, analytical and computational skills.
Strong communication skills for a variety of audiences (other technical staff, senior management and regulators) both verbally and in writing.
Capability to multi-task and finish work within strict timelines and provide timely requests for information and follow-up questions.
Ability to work independently on complex model validations from start to finish.
Skill in managing relationships with key model stakeholders
Eagerness to contribute collaboratively on projects and discussions
Perpetual interest in learning something new, but being comfortable with not knowing the all the answers
Attention to detail in both analytics and documentation
Aptitude for synthesizing data to form a story and align information to contrast/compare to industry perspective
Intellectually curious, who enjoy solving problems
Desired Skills:
Hands on industry experience in building large scale end-to-end machine learning systems using a combination of big data tools and programming languages like Scala, Java and/or Python
Knowledge of parallel and distributed computing frameworks such as Hadoop, Spark, MPI, using GPUs
Experience in machine learning areas such as recommender systems, NLP, pattern recognition, predictive modeling, Artificial Intelligence.
Experience implementing machine learning algorithms such as support vector machines, decision trees, logistic regression, clustering, neural networks, graphical models etc
Data exploration and preparation using SAS, Spark, Python ,SQL or R or any statistical tool
Working experience in financial model validation team would be added advantage
Prepare detailed documentations for projects for both internal and external that complies regulatory and internal audit requirements
Keep updated with the latest in the Data Science community and leverage new capability for the bank
Knowledge of banking industry, terminologies and products in at least one of the LOB such as credit cards, mortgage, deposits, loans or wealth management etc.
Knowledge of any functional area such as risk, marketing, operations or supply chain in banking industry.
Instrumental in bringing new approaches to the table, create white papers and present in conferences
