Lynx Analytics was founded in 2010 by a group of INSEAD students and professors with a strong research background in graph analytics. Several of our founders since then became professors and faculty directors of analytics centers at leading US universities. Our founding purpose? To apply graph theory to simplify and solve complex, real-world business problems.
Our mission has evolved over the years, and we currently offer a range of cutting edge data analytics and AI solutions to help companies transform their operations and optimise their commercial performance. Back then, graph theory was mostly the purview of social networking sites. We wanted to expand this technology and help companies leverage their communities to unlock greater growth.
Lynx has offices in Singapore, US, Hong Kong, Hungary, and operations in several other countries such as Canada, Germany, Indonesia. We work with some of the world’s largest companies and are constantly looking to expand our knowledge base and geographical footprint. Lynx Analytics’ technology is deployed with various Clients internationally and has significant growth potential.
We have a diverse and inclusive global team comprising Professors, PhDs, MSc’s, and MBAs from Ivy Leagues, INSEAD and NUS with a broad spectrum of experience in start-ups and blue-chip companies (Google, Databricks, ZS, Abbvie, Amgen, Vodafone, Morgan Stanley, Palantir, Katana Graph to name but a few). It is the combination of our industry insight and experience, scalable proprietary technology, and highly qualified people that drives our compelling value proposition.
We are looking for ambitious, innovative, empathetic and relentless team players to explore the career opportunities that we offer as we continue to scale our operations.
- Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation
- Work closely with data scientists and analysts to create and deploy new features
- Write efficient and well-organized software to ship products in an iterative, continual-release environment
- Monitor and plan out core infrastructure enhancements
- Ability to optimize model performance, push model into performance, track and test, refactor code
- Contribute to and promote good software engineering practices across the team
- Communicate clearly and effectively to technical and non-technical audiences
- Actively contribute to and re-use community best practices
- Relevant tertiary qualification
- 2 to 4+ years of experience with at least 1 year of experience in Machine Learning Engineering
- Strong knowledge of Python and SQL
- Good problem-solving skills
- Familiarity with cloud provider solutions such as AWS / GCP / Azure and severless code development
- Experience with data pipeline and workflow management
- Experience in software engineering methodology (i.e. code reviews, unit testing etc.)
- Experience developing predictive models in a production environment, MLOps and model integration into larger scale applications