AVP, Machine Learning Engineer, Group Consumer Banking and Big Data Analytics Technology, Technology & Operations
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.
Apply Now We offer a competitive salary and benefits package and the professional advantages of a dynamic environment that supports your development and recognises your achievements.
