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AVP, Data Scientist, ITT-TM Ops, Technology & Operations
AVP, Data Scientist, ITT-TM Ops, Technology & Operations
As an AVP, Data Scientist within the ITT-TM Ops, Technology & Operations team, you will be responsible for building and deploying end-to-end data science solutions using large and complex financial datasets. This role involves developing scalable machine learning architectures, automating CI/CD pipelines, and optimizing ML algorithm efficiency.
About the role
About the 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.
Responsibilities
- Work with large and complex financial datasets to build end-to-end data science solutions.
- Deploy machine learning products into production and perform sub-system integration as required
- Refactor and document code into reusable libraries, APIs, and tools
- Automate CI/CD pipeline for model training, testing and deployment
- Assist organizational adoption of industry leading ML ecosystem (feature/eval store, model zoo)
- Optimize machine learning algorithm efficiency (GPU distributed computing, concurrent programming)
Requirements
- Experience building scalable machine learning system architectures (microservice, distributed) and big-data pipelines in production
- Good understanding of the data science production life cycle with demonstrable experience working with structured, semi-structured and unstructured data.
- Excellent software skills (Python, SQL, bash) and knowledge in design patterns and code optimization
- Good grasp of Machine Learning models and concepts, their mathematical underpinnings, and trade-offs (model selection, tuning, problem formulation, drift, semi-supervised learning)
- Experience using NLP techniques (NER, Sentiment Analysis, Topic Modelling, transformers)
- Experience using machine learning frameworks (TensorFlow, Pytorch, Spark, Hydra)
- Familiar with software development best practices and tools (Agile, TDD, Jira, Jenkins, Git)
Good-to-have
- Familiar with Linux operation system
- Financial domain expertise (Treasury & Markets)
- Experience with statistical modelling and time series analysis
Professional Qualification
- A Bachelor’s degree in Data Science or Computer Science (or equivalent experience)
- 3 – 8 years of data science/ software engineering exposure
- Have experience in applying machine learning models
- Able to perform unix / linux scripting
- Writing of documents that clearly explain how algorithms should be implemented, verified and validated