VP/AVP, Lead Data Science Analyst, Strategic Initiatives, Regional Consumer Banking Operations Management, Technology & Operations - (WD37034)

Key Skills

Java Programming, Analytical and Problem solving, Python Programming, SQL, Agile methodologies, Machine learning techniques, Data science techniques, Data Mining, Deep Learning, Algorithms, Data Modelling, MS Dynamics

Job Description

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.

Job role

As the Lead Analyst on Data Science, you will lead the development and solutioning of the Customer Science Platform. The focus is on real time predictive analytics to derive actionable insights to intervene positively in the customer’s digital journey. You will manage data science deliverables, provide thought leadership on advanced analytics and hyper-personalization, and mentor team members. You will be required to collaborate with business and technology teams in the delivery of data science/analytical capabilities to achieve the targeted business outcomes. You will need to be creative and resourceful and will utilize traditional statistical methodologies as well as newer techniques such as computational statistics, data mining, big data capabilities and to incorporate Machine Learning/AI Techniques.

Responsibilities

• Lead the development and solutioning of real time predictive analytics stack to derive actionable insights leading to customer interventions.
• Perform full life-cycle Data Scientist/Analyst activities, including conceptualization to operationalization.
• Lead in ‘connecting the dots’ from the data, to modelling, to insights, to actionable outcomes, to solving business problems.
• Apply data science to solve business problems; data mining techniques, doing statistical analysis, building high-quality prediction systems, and use deep learning techniques.
• Mentor and develop team members across Data Scientists, Analyst, Visualizers, and Engineers.
• Collaborate with cross-functional Business and Technology teams to identify and prioritize actionable, high-impact insights in the customer servicing domain.
• Lead in customer behavioural analytics with bias towards actionable insights.
• Provide thought leadership based off current business and industry trends in advanced analytics.

Requirements

• University Degree or higher in Applied Statistics, Data Mining, Machine Learning, Computing or related Quantitative Discipline, with a strong background in statistical concepts and calculations.
• Proven ability on structured problem solving, data-driven analysis, real time analytics, and deriving actionable outcomes in solving business problems.
• Proven track record of leading a team in handling advanced analytics.
• Deep and practical understanding on implementing high performance, well-behaved analytics applications; data ingestion, feature engineering, model selection, training, validating and deployment.
• Deep understanding of statistical and predictive modelling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms.
• Curious and have a strong appetite for intellectual challenges.
• Keen on learning, on data, solving for scalability, and being agile.
• Excel at making complex concepts simple and easy to understand.
• Comfortable leading in a fast paced, hypothesis driven, collaborative, and iterative engineering environment.
• Driven, strategically focused, self-starter and organized with strong attention to detail.
• A good team player with excellent communication skills.

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