Machine Learning Engineer

Company Description

Revolutionising Marketing-Tech in ASEAN; making Data simple, accessible and actionable. We aspire to "Connect 650mil ASEANs Consumers".

Founded in 2014 as a boutique, research & advisory firm. By 2018, we transformed ourselves into a data analytics & intelligence firm focusing on Consumers, Media & E-Commerce. Since 2017, approximately RM 30mil+ have been invested for R&D purposes and Market seeding.

Currently, Dattel operates in Malaysia (HQ), Indonesia and Thailand, with a corporate holding entity out of Singapore.

Role Description

This is a full-time hybrid role for a Machine Learning Engineer located in Petaling Jaya but with the flexibility for some remote work. We’re looking for an experienced machine learning engineer to join the industry transformation using analytics and machine learning (ML) to help us improve various business outcomes and drive innovation. You will play an active role in bringing Dattel’s product offerings to the next level, understanding the consumer and marketing experience through data, to ensure a personalized and effective marketing experience for our customers and campaign audience.

You will join a multidisciplinary team helping to shape our data and analytics strategy and showcasing the potential for analytics and ML in marketing and advertising space, from early-stage solutions towards operationalisation. You will be responsible to collect and prepare data for analytics and ML solutions at scale, productionize them by building adequate inference endpoints or interfaces for the model consumptions, capturing feedback via active monitoring and enable continuous integration and delivery via MLOps or ML pipelines automation using appropriate ML platform.

Responsibilities

• Lead end to end analytics and ML product offering development and oversee ML solution lifecycle management, which include deployment, operation, monitoring, optimisation, and retirement.
• Collaborate with product manager, data engineers, software developers and solution architect to deploy analytics and ML solutions into production.
• Develop, deploy and operationalize analytics and ML solutions to ensure service performance, scalability, and availability meeting service level agreement.
• Optimize and fine tune analytics and ML products with professional software engineering skills.
• Work on functional design, process design (including data collection and scenario design, flow mapping), prototyping, testing, training, and defining support procedures from scratch, in collaboration with product, engineering and executive leadership.
• Advise C-suite executives and business leaders on a broad range of technology, strategy, and policy issues associated with analytics and ML.

Qualifications

• At least Bachelor’s degree in relevant technical fields such as Computer Science or Engineering, Artificial Intelligence or equivalent.
• 5-7 years of experience in analytics and ML development and production environment.
• Excel in SQL, Spark, Python or other relevant programming languages for production.
• Solid experience in cloud technologies such as AWS, as well as containerization such as Docker, Kubernetes, Kubeflow, and some DevOps tools (e.g. Helm, etc.) to implement and maintain CI/CD pipelines.
• Solid foundation in analytics such as multivariate analysis, statistical inference, experimental designs, etc.
• Excel in ML relevant libraries and frameworks such as scikit-learn, tensorflow, pytorch, etc.
• Knowledge in recommender system, NLP, image processing, digital signal processing, variational autoencoders, diffusion model, GAN and RAG.
• Experience in both RDBMS and NoSQL databases.
• Familiarity in dashboard, visualization and data storytelling.
• Familiarity in versioning tools (e.g. Github, CodeCommit) and their CI/CD pipelines tools (e.g. Github action, AWS pipeline).
• Familiarity in ETL tools such as Airflow, Alteryx, Luigi, Nifi, Glue as well as ingestion tools (e.g. Kafka, Flink, Kinesis, etc.).
• Familiarity in various data or foundation models sourcing and data labeling mechanism and tools.
• Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice patterns.
• Understanding in data lake and data warehousing concepts and solutions.
• Ability to work both independently and collaboratively, in a fast paced and demanding environment.
• Excellent interpersonal and communication skills.
• Problem-solving aptitude.
• Experience in ML solution development lifecycle and/or consumer and marketing analytics is a plus.