Join one of the world's most renowned global banks and trusted brand with over 200 years of continuously evolving financial services worldwide. You will work alongside some of the smartest minds in the industry who are excited to share their knowledge and to learn from you.
Contract Duration: 6+ Months
Required Skills & Experience
- 5 to 8 years of data analysis.
- 2+ years as a Data Scientist.
- Knowledge and experience of a variety of exploratory data analysis, predictive modelling, anomaly detection and their real-world advantages/drawbacks.
- Experience using statistical computer languages like Python, R, etc.
- Working to deep knowledge of financial products and automation solutions.
- Experience in predictive model a/b testing and training models.
- Experience in complex data extraction techniques and data cleansing methodologies.
- Ability to deconstruct descriptive data into meaningful categories for predictive modeling and visual analysis.
- Experience in all the phases of a software development lifecycle project including requirements gathering, analysis, design, and implementation through agile and/or waterfall and/or hybrid methodologies.
- Ability to develop and manage a comprehensive program plan and dependencies
- Able to work with large and complex data sets to evaluate, recommend, and support the implementation of business strategies.
- Experience in solving analytical problems using quantitative approaches with a passion for data & statistics.
Desired Skills & Experience
- Prefer machine learning experience.
- Prefer experienced with data visualization tools.
What You Will Be Doing
- Responsible for documenting data requirements, data collection/processing/cleaning, and exploratory data analysis; which may include utilizing statistical models / algorithms and data visualization techniques.
- Identify and compile data sets using a variety of tools (e.g. SQL, Access) to help predict, improve, and measure the success of key business to business outcomes.
- Mine and analyze large sets of data.
- Identify gaps and opportunities for improvement.
- Leverage this data to develop strategic roadmaps.
- Define and deploy metrics for measuring process improvement.
- Institute feedback models to enable continuous process improvement.
- Conduct research and publish artifacts to drive simplification.
- Review/author operational procedures.
- Distill information provided by subject matter experts and other partners into executive-level narratives.
- Establish foundational and execution work streams for process improvement and automation initiatives.
- Partner with Domain and Technical architecture teams to drive efficiency.