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AVP, Machine Learning Engineer, Group Consumer Banking and Big Data Analytics Technology, Technology & Operations

Job Title: AVP, Machine Learning Engineer, Group Consumer Banking and Big Data Analytics Technology, Technology & Operations
Contract Type: Permanent
Location: Singapore
Industry:
Reference: WD18763
Contact Name: Celine Liew
Job Published: March 01, 2021 12:40

Job Description

Business Function

Group Technology and Operations (T&O) enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group T&O, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.

Business Function

Group Technology and Operations (T&O) enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group T&O, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.

Roles & Responsibilities

  • Build and improve machine learning and analytics platform.
    • Apply cutting edge technologies and tool chain in big data and machine learning to build machine learning and analytics platform.
    • Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
    • Keep innovating and optimizing the machine learning workflow, from data exploration, model experimentation/prototyping to production.
    • Provide engineering solution and framework to support machine learning and data-driven business activities at large scale.
    • Verifying data quality, and/or ensuring it via data cleaning
    • Defining validation strategies
    • Defining the preprocessing or feature engineering to be done on a given dataset
    • Defining data augmentation pipelines
    • Training models and tuning their hyperparameters
    • Supervising the data acquisition process if more data is needed
    • Perform R&D on new technologies and solutions to improve accessibility, scalability, efficiency and us abilities of machine learning and analytics platform.
    • Deploying models to production
  • Work with data scientists to build end-to-end machine learning and analytics solution to solve business challenges.
    • Turn advanced machine learning models created by data scientists into end-to-end production grade system.
    • Build analytics platform components to support data collection, exploratory, and integration from various sources being data API, RDBMS, or big data platform.
    • Optimize efficiency of machine learning algorithm by applying state-of-the-art technologies, i.e. distributed computing, concurrent programming, or GPU parallel computing.
    • Support initiatives for data integrity and normalization
  • Establish, apply and maintain best practices and principles of machine learning engineering.
    • Study and evaluate the state of the art technologies, tools, and frameworks of machine learning engineering.
    • Contribute in creation of blueprint and reference architecture for various machine learning use cases.
    • Support the organization in transformation towards a data driven business culture.
    • Contributing to the overall solution design and architecture
  • Work Relationships
  • Internal
    • Work closely with data scientists, business team, and project managers to provide machine learning and data-driven business solution.
    • Collaborate with other technology teams to build platform and framework to enable machine learning and data analytics activities at large scale
    • Support overall project and team in the capacity of a ML Engineer.
    • Managing available resources such as hardware, data, and personnel so that deadlines are met
  • External
    • Maintain engineering principles and best practices of machine learning framework and technologies.
    • Document user requirements using Agile Frameworks
    • Working with Project Lead/Scrum Master to rapidly analyse data requirements and identify gaps.
    • Act as key conduit between development team and product owner.

Requirements:

  • At least 4 years+ of ML development or system design working experience
  • 2+ years of experience in machine learning system or data science research
  • Experienced as both a Data Scientist and Machine Learning Engineer
  • Experienced working in Software Engineering, DevOps and Data Engineering
  • Proficient in Python Data Science libraries such as but not limited to Numpy, Pandas, Numba & Scikit-learn.
  • Startup experience & Fintech Experience is a plus
  • Experienced/knowledgeable in A/B testing, uplift modelling for digital marketing. Reinforcement learning and Multi-Armed Bandits is a plus
  • Proficient in writing ETL using Airflow
  • Proficient in writing orchestration DAGs for Machine Learning Lifecycle Management
  • Proficient in creating dashboards with Streamlit & Grafana, Kibana is a plus
  • Experienced with ELK and logging with Python to ELK
  • Experienced in writing with Python: Flask, REST and GraphQL API endpoints or Middleware
  • Proficient in writing Multi-threading, Asynchronous and Multi-processing code in Python
  • Experienced with creating machine learning projects from start to finish from model creation to model deployment to production with proper CICD processes and Model observability and logging
  • Experienced in Image recognition for videos such as image annotation and OCR for PDF extraction tasks.
  • Experienced in MLOPS tools such as MLFlow for Machine learning cycle
  • Experienced in Data science enablement tools such as Kubeflow, with experience in Jupyter Notebooks and containerization of Machine learning models with serving tools such as KFServing and Seldon Core.
  • Proficient in writing Docker Files and creating Docker containers
  • Core professional expertise includes: Platform Architecture, Data Pipelines Architecture, Infrastructure Deployment and Management
  • Able to support existing and potential customers with requirements capture, solutions architecture, system design, solution prototyping
  • Experience in Kubeflow or Cloudera Data Science Workbench, is a big plus.
  • Experience with building traditional Cloud Data Warehouses, Data Lakes. Close and intensive work on previous projects with Containers and Resource Management systems: Docker, Kubernetes, Yarn.

 

We offer a competitive salary and benefits package and the professional advantages of a dynamic environment that supports your development and recognises your achievements. 

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