You will work with the firm’s rich data pool from both internal and external sources using Python/Spark via AWS and other systems. You are also expected to derive business insights from technical results and be able to present them to non-technical audience.
Job responsibilities
- Proactively develop understanding of key business problems and processes
- Execute tasks throughout a model development process including data wrangling/analysis, model training, testing, and selection.
- Generate structured and meaningful insights from data analysis and modelling exercise and present them in appropriate format according to the audience.
- Collaborate with other data scientists and machine learning engineers to deployment machine learning solutions.
- Carry out ad-hoc and periodic analysis as required by the business stakeholder, model risk function, and other groups.
Required qualifications, capabilities, and skills
- 2-3 years of relevant experience post Advanced degree (MS, PHD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, and Econometrics)
- Practical expertise and work experience with ML projects, both supervised and unsupervised.
- Proficient programming skills with Python, R, or other equivalent languages,
- Demonstrated experience working with large and complicated datasets.
- Experience with broad range of analytical toolkits, such as SQL, Spark, Scikit-Learn, and XGBoost.
- Excellent problem solving, communication (verbal and written), and teamwork skills;
Preferred qualifications, capabilities, and skills
- Experience with graph analytics and neural network (Tensorflow, Keras).
- Experience working with engineering teams to operationalize machine learning models.
- Familiarity with the financial services industry.