Senior/ Data & Machine Learning Engineer

As the world's leading owner and operator of LPG carriers with five decades of operating experience, BW LPG promotes competitive, sustainable solutions to secure value for society and our stakeholders.

Our global footprint spans seven countries with 17 nationalities represented across our workforce. Our fleet of 39 Very Large Gas Carriers (VLGC) offer a total carrying capacity of over 3 million CBM. From spot voyages and time charters to Contracts of Affreightments (CoAs), our emphasis on flexible, reliable service has earned the trust of leading oil companies as well as trading and utility companies. More information about BW LPG can be found at www.bwlpg.com.

BW LPG is associated with BW Group, a leading global maritime company involved in shipping, floating infrastructure, deepwater oil & gas production, and new sustainable technologies. Founded in 1955 by Sir YK Pao, BW controls a fleet of over 490 vessels transporting oil, gas and dry commodities, with its 200 LNG and LPG ships constituting the largest gas fleet in the world. In the renewables space, the group has investments in solar, wind, batteries, biofuels and water treatment. BW LPG, as a member of the BW Group, is one of Forbes World's Best Employers 2023.

Summary:

The Senior/ Data & Machine Learning Engineer is a hybrid role focusing on developing our modern Lakehouse including pipeline development, and the deployment of machine learning models.

This position combines the responsibilities of a Data Engineer and Machine Learning Engineer, ensuring robust data pipelines and scalable deployment of ML models.

The role involves building data ingestion and transformation processes, deploying machine learning models, and working with DevOps and CI/CD for automated deployments and monitoring.

Collaborating with Business Analysts to ensure business requirements are translated into data solutions.

Working with Data Analysts to deliver accurate and reliable datasets that power impactful analytics and dashboards.

Partnering with other Data Engineers and Analytics teams (e.g., Quantitative Research team) to support advanced analytics and provide datasets for reporting and insights.

They collaborate with System Analysts and the Business to understand the business logic and data lifecycle within our systems and translate this into technical analytical solutions.

Responsibilities

• Design and maintain data pipelines: Design, build, automate, and optimize ETL/ELT pipelines to support data flow, transformation, and availability across the Lakehouse platform.

• Collaborate on data storage and solutions: Partner with analysts to create scalable storage solutions, optimizing data structures for both analytics and ML applications.

• Implement data quality and governance standards: Establish monitoring and enforce data quality checks, ensuring data consistency, reliability, and compliance.

• Optimize and maintain model performance: Continuously monitor deployed models for performance, retraining or adjusting them as needed to ensure sustained accuracy.

• Deploy and monitor machine learning models: Design and implement CI/CD pipelines for model deployment, ensuring efficient and reliable delivery of ML models.

• Mentor junior team members: Guide and foster the professional development of junior data engineers, enabling their contribution to the team and ownership of project deliveries.
  

Expertise:

• Collaboration and Communication Skills: Excels at working in a team to align data solutions with business needs, focused on creative problem-solving in a fast-paced environment.

• Analytical Thinking & Communication: Strong skills in analyzing and translating data-driven insights and requirements to both technical and non-technical stakeholders.

• Data Pipeline Development Skills: Proficiency in designing and implementing data pipelines to support reliable data ingestion and transformation using Databricks and Spark.

• Data Modeling Expertise: Advanced SQL and Python skills for complex queries and data modeling, enabling efficient data access and transformation.

• Python & Model Deployment Proficiency: Expertise in Python for deploying models and developing APIs for serving predictions.

• Spark & Azure Databricks: Skilled in using Spark on Databricks for processing large-scale datasets and supporting machine learning workflows.

Knowledge of:

• CI/CD Pipeline Creation (incl. for ML Models): Experience in creating CI/CD pipelines, enabling automated testing, validation, and deployment of data pipelines and machine learning models using GitHub and GitHub Actions with Databricks.

• Monitoring & Model Maintenance: Ability to set up monitoring tools for models, track metrics (accuracy, latency), and maintain model performance.

• IaC for Infrastructure Automation: Experience in automating cloud infrastructure management for consistent deployment and scaling using Infrastructure as Code tools like Terraform.

• Power BI Integration: Basic understanding of Power BI for creating visualizations of model performance and metrics for stakeholders.

• Agile Methodologies in Data Engineering: Understanding of agile frameworks to work within iterative sprints and collaborate with cross-functional teams

Experience:

• At least 3 Years in Data Engineering: Proven experience building and maintaining data infrastructure, with a deep understanding of data engineering principles and best practices.

• 1 Year in Machine Learning Engineering: Track record in managing the lifecycle of machine learning models, including deployment, monitoring, and retraining.

• Business & Data Transformation: Experience working in supporting business transformation and process improvement into Data-Driven organizations and systems.

• Agile Mindset and Experience: Thrives in Agile Environments, with a strong resilience, adaptability and responsiveness to change in fast-paced settings.
  

The position will be based in Singapore.

Only shortlisted candidates will be notified.