Job Description

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We are living in dynamic times. Technology is reshaping how we live, and we want to use it to redefine how financial services are offered. Digibank is a Grab-Singtel consortium, aimed at enabling the underserved groups to easily access transparent financial services that are embedded in their everyday activities, helping them achieve a better quality of life. We are incredibly excited to build a Digital Bank with the right foundation using data, technology and

trust to solve problems and serve customers

Get to know the Role:

\xe2\x97\x8f Design and implementation of Machine Learning and Big Data platforms, pipelines and tooling.

Build appropriate inference interfaces for ML model consumption, deploy ML models to production environments, enable ML Ops for continuous delivery and automation of ML pipelines, build and sustain AI production platforms.

Create successful handshakes between AI Algorithms and downstream teams.

Continuously improve, optimize AI models and tune workflows to enhance model performance, accuracy, efficiency, and scalability.

Develop and implement data ingestion, cleansing, transformation and feature engineering pipelines to support ML models.

Develop and implement automated testing, monitoring, alerting and retraining of ML models in production to ensure model performance and quality.

Ensure high quality models and seamless integration, which includes model accuracy, automated quality checks, API latencies, deployment time etc.

Demonstrate a good grasp of architecture and best practices in integration of AI / Machine Learning models into backend services, with smooth functioning and scalability of ML applications in production environments.

Handle machine learning operations by working collaboratively with data scientists, data engineers, product managers and cross-functional teams.

With a start-up mentality and able to work in a fast-paced, hands-on and results oriented environment.

Implement and manage a high standard of procedural documentation.

\xe2\x97\x8f Stay current on cutting edge machine learning approaches and latest best practices in machine learning architecture and integration, and propose improvements to our existing processes and infrastructure.

Must Haves:

\xe2\x97\x8f Significant relevant experience (4+ years of experience) in designing and implementing machine learning and big data platforms, pipelines and tooling.

Work experience in AWS based MLOps or DevOps

Extensive hands-on experience in coding and modelling skills in Spark, Python, R, SQL, Presto, Hive proficiency, AWS cloud platform, git and CI/CD concepts

Proficiency in AWS compatible ML Technologies \xe2\x80\x93 AWS - Sage maker, Tensorflow, Amazon ML Feature Store

Experience in AIOps stack (e.g. MLFlow, Jenkins, Kubernetes etc)

Highly skilled with API design and deployment

\xe2\x97\x8f Advanced degree preferred: Masters degree in Computer Science, Applied Mathematics, Statistics, Machine Learning, or a related quantitative field.

\xe2\x97\x8f Deep technical and data science expertise, including experience in the following:

\xe2\x97\x8f Analytical methods: statistical modeling (e.g., logistic regression, time series, CHAID, PCA), supervised machine learning (e.g., random forests, neural networks), unsupervised learning, design of experiments, segmentation/clustering, text mining, network analysis and graphical modelling, optimization, simulation

\xe2\x97\x8f Experience building in-production models, including associated scripting, error handling and documentation

\xe2\x97\x8f Understanding of trade-offs between model performance and business needs.

\xe2\x97\x8f Strong business acumen, inherent curiosity about data, stakeholder management and project management skills to prioritize & manage multiple priorities in a fast-paced and multidisciplinary environment

\xe2\x97\x8f Highly self-driven, demonstrate critical thinking, team player & fast learner

\xe2\x97\x8f Work experience and knowledge of more than one domain is a plus - Risk Analytics, Marketing Analytics, Telecom analytics, Retail analytics, Fraud analytics etc.

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