You will be part of a world-class team enabling enterprises to harness the power of AI/ML to transform their businesses to be more data-driven and responsible in their adoption of artificial intelligence.
As part of the data science delivery team, you will help to solve challenging problems through the use of machine learning and deploy models into production using our Bedrock platform.
In this role, you can expect to:
- Help our customers to use their data to improve product, operations and decision making
- Develop machine learning models and/or design data-driven and algorithmic solutions to solve customer business problems
- Implement data pipelines and productionise models as microservices in the cloud, using our Bedrock platform
You may be a good fit if:
- Have 2+ years experience in data science and quantitative analysis (preferably in an engineering or product role).
- Strong programming skills and experience analyzing complex datsets (eg. Python, Spark, SQL).
- Experience with ETL and implementing efficient data pipelines Expertise in a range of ML methods (classifiers, recommendation systems, time series, optimisation, etc) and frameworks (e.g. TensorFlow, Pytorch).
- Experience in end-to-end data science workflows and able to prototype ML models and collaborate with engineers to productionise solutions
- Degree in a quantitative domain such as Computer Science, Machine Learning, Statistics, Operations Research, or similar. Masters and PhD is a plus.
- You are confident, proactive, organised, with strong communication ability and a willingness to work with a multidisciplinary team.
- You are prepared to go outside your comfort zone with a strong growth mind-set.
We founded BasisAI to help develop technology to enable responsible AI solutions for enterprises. We are a seed stage, 20-person startup and have raised USD 6M in funding from Sequoia and Temasek.
We are proud of the team we have created. We pay competitively, and believe that all hires should have an equity stake in the company. We win if everyone wins together.