Senior Data Scientist
Some careers open more doors than others.
If you're looking for a career that will unlock new opportunities, join HSBC and experience the possibilities. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further. Wealth and Personal Banking
is our new global business combining Retail Banking and Wealth Management; and Global Private Banking, to become one of the world's largest global wealth managers with USD1.4 trillion in assets. Across Asia, where wealth pools are growing faster than in any other region, HSBC's wealth revenues grew 12% in 2019 (year-on-year) to USD5.7 billion. Our dedicated colleagues serve millions of customers worldwide across the entire spectrum of private wealth, ranging from personal banking for individuals and families, through to business owners, investors and ultra-high-net-worth individuals. We provide products and services such as bank accounts, credit cards, personal loans and mortgages, as well as asset management, insurance, wealth management and private banking, that best suit our customers' needs.
We are currently seeking an ambitious individual to join our Wealth and Personal Banking
team in the role of Senior Data Scientist
working together with colleagues to define, manage and achieve divisional business targets. Pricipal Responsibilities:
This is a key position to manage end-to-end data science initiatives from ideation, design & development, deployment and commercialization in an agile working model. This role involves delivering Data Science analysis to support business decisions for various WPB Business Units of HSBC; developing Advanced Analytics components to be included in innovative applications by creating prototypes and developing solutions based on advanced analytics, leveraging internal data sources as well as investigating the potential of external data sources.
The role holder will be accountable to:
- Develop predictive models and segmentation models for the purpose of improving customer relevancy and experience
- Leverage existing data and identify new data sources to drive proposition development
- Monitor and assess portfolio performance and actively identify portfolio opportunities or risks
- Establish a robust tracking discipline to compare and share best practices across countries
- Support Customer Relationships Management in developing key lifecycle contact journeys through Next Best Action program
- Lead a dedicated team as well as project-manage other analytic resources at country and region level
- Create an environment where analytics capability is increased to identify and drive increased revenue generation from customer data to support Retail Banking and Wealth Management products and propositions
- Perform ad-hoc exploratory statistics and data mining tasks on diverse datasets from small scale to "big data"
- Select features, building and optimizing classifiers & recommender algorithms using machine learning techniques
- Data mining using state-of-the-art methods
- Extend company's data with third party sources of information when needed
- Enhance data collection procedures to include information that is relevant for building analytic systems
- Process, cleanse, and verify the integrity of data used for analysis
- Manage end-to-end data & analytics initiatives from ideation, design & development, deployment & commercialization in an agile working model; able to manage / influence a wide range of partners / stakeholders from technology, business owners as well as senior management
- Translate highly complex analytics solutions to business language, & proactively manage business stakeholders to maximize value from analytics initiative
- Drive commercialization of analytics solutions by defining and tracking value-add in tangible, quantifiable measure such as incremental revenue generation, or cost avoidance, or cost reduction, etc
- Actively connect dots and identify risks / challenges / gaps and provide effective problem solving in complex projects where data & analytics play significant roles
- Mine through large data sets to uncover trends, insights, and opportunities and apply relevant analytical methods / algorithms to get actionable insights from HSBC's internal (both structured and unstructured) and external data sources
- Delivering modelling/analytics to support securities portfolios decision making
- Create helper functions to automate frequently encountered wrangling and feature engineering tasks
- Work with data engineers to enhance the analytic data infrastructure and develop enterprise analytic data marts on Google Cloud Platforms.
This role will be critical to co-lead messaging and customer engagement initiatives (Next Best Action programme) as well as Machine Learning & Artificial Intelligence (ML & AI) capabilities, which are multi-million $ investments. Qualifications Requirements:
- Proven experience in process and analysis of large amount of data using one of these: Python, R, SQL or SAS; on environments such as AWS, Google cloud or Hadoop
- 3+ years of experience in machine learning model building, training, tuning, and deployment
- Mandatory to have both business understanding and hands-on coding proficiency for the most commonly used machine learning algorithms, such as Random Forest, LightGBM, XGBoost, Clustering methods, ADAM/Bayesian optimization, CNN, RNN, Transformer model, Time series analysis models, etc.
- Hands-on experience in one of AWS cloud, GCP or Azure preferred
- NLP knowledge and hands-on coding capability
- Hands-on engineering capability in one or more of below technologies: Kubernetes/docker/Kubeflow, Spark/Hadoop, Flask/FastAPI/Nginx/gunicorn
- Front-end design/development experience for mock-up UI
- Machine Learning CI/CD DevOps development experience
- Experience with 1+ AGILE methods: Scrum, Kanban, SAFe, Pair programming
- Demonstrated experience in one or more of: customer segmentation, digital marketing, data science, portfolio analytics, use of open-source data in analyses
- Experience with using AGILE tools: Jira, Confluence, SLACK
- Demonstrated proficiency in Artificial Intelligence / Machine Learning algorithms and kits; hands-on use of Tensorflow, DataRobot or Sagemaker (or similar tools)
- Having Bachelor degree in one of these preferred disciplines: economics, computer science, data science, analytics, statistics, econometrics, management information system, communications, operations research.