Who You Are
You have the heart of an adventurer. You’re always on the lookout for the next big thing. This trait of yours is what got you into the field of data science in the first place (and you’re so glad you did)!
You’re a curious one. You aren’t afraid of asking the difficult questions. Digging beneath the surface to get to the root of any problem comes naturally to you. Your favourite part of the job? Finding solutions. You particularly enjoy the creative, analytical and investigative process necessary to extract meaning from data. These insights, which go on to inform, and even transform, key business decisions and processes, highlight the power of raw data. And it is data scientists like yourself who hold the key to unleashing its true potential.
You are wired to thrive in the unknown. Ambiguity thoroughly excites you. You tend to look beyond the familiar in search of new opportunities. Sure, it means hard work, but you aren’t one to shy away from rolling up your sleeves and getting your hands dirty. It’s no wonder others look to you for leadership and direction.
You know you can’t succeed alone. You’re connected to the larger analytics community – a resource you not only value, but also continue to invest in by mentoring the next generation of data scientists.
- You got the chops to:
- Propose novel solutions to improve existing enterprise analytic platforms.
- Develop new advanced analytic capabilities, while adopting best-in-class techniques and practices.
- Formulate end-to-end advanced analytic solutions.
- You have an advanced degree with a strong focus in machine learning, data mining, or statistical and mathematical modelling.
- You have at least 5 years of related experience in data science and machine learning.
- You know the drill. You are familiar with analytics and machine learning platforms and how they integrate with upstream and downstream data and production systems.
- You’ve worked with technology teams to improve the user experience of an analytics platform.
- You’re no stranger to managing complex and cross-department projects.
Ideally, You Should Have
- Full stack data science experience covering end-to-end problem formulation, developing training pipeline and deploying solution to production.
- Familiarity with developing machine learning pipelines following dev practice such as versioning, branching, merging, linting, testing and writing codes in structured and modularised way.
- The technical know-how: SQL, Python, PySpark, ML packages and tools.
- Practical experience in cloud-based analytics tools.
Singapore-DBS Asia Central