
Associate Vice President - Data Science
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The AVP ā Data Science will lead the development of AI/ML solutions that drive dataādriven decisionāmaking across customer engagement, marketing, retention, risk, and product functions. The role requires strong analytical expertise, handsāon experience with openāsource technologies, and the ability to build interactive applications and deploy models in both batch and realātime environments. The candidate will ensure highāquality delivery, effective stakeholder communication, and adherence to best practices for reproducible, scalable data science.
What You Will Do:
⢠Work with Data Science team to translate given business problem into analytical use-cases with defined outcomes to develop, implement and test most appropriate algorithms for a given use-case.
⢠Work closely with Business Analyst for requirement gathering/understanding and work with Data Engineers to build data pipelines and automate/production Alize complex ML models to insights and recommendations.
⢠Strong conceptual understanding of machine learning algorithms including multi-variate regressions, classification algorithms, time series techniques, clustering, NLP, Image Processing, and optimization models etc.
⢠Ensure high coding standards as well as designing standards to ensure reproducibility.
Drive innovation by enhancing existing solutions and designing new ones and build collaboration and awareness in the bankās analytics community
⢠Work closely with various business units/stakeholders to identify and streamline the AI-ML use-cases.
⢠Deliver end-to-end AI-ML models from development to deployment with delivery planning, communications with stakeholders, and ensuring efficient usage of the models by the business as recommendations for improving decision touchpoints.
⢠Ensure high coding standards, peer-review, and transparency in the work with the line of reporting.
⢠Support ad-hoc requirement in terms of MIS development, building and analyzing SAS Data Models, streamlining the reporting process through various reporting and analytical tools.What We Are Looking For:
⢠Overall 7+ years of experience in analytics, data science or similar function
⢠3-4 years of experience in banking analytics
⢠Python and SQL experience required
⢠Worked on end-to-end ML model deployment
⢠Machine Learning / Deep Learning / Time-Series / Optimization and Customer AnalyticsTechnical Skills:
⢠Strong coding skills using Python.
⢠Knowledge on Retail Banking Products
⢠Deployment experience (various databases, server/cloud environment: AWS, Azure, and APIs, ODBCs, web apps)
⢠Excellent knowledge of Banking Functional Knowledge (major plus)
⢠Good written, oral communication, documentation skills with ability to communicate effectively with stakeholders.
⢠Expert-level proficiency in Python with SAS/R/Spark as plus
Whatās In It For You:
⢠Pay for performance culture (Competitive and performance-linked compensation)
⢠Provision of medical care plan for your family and you.
⢠Comprehensive Life insurance plan.
⢠Diverse workforce and inclusive culture.
⢠Career development and growth opportunities by design.
⢠Work with the best minds in the field.
⢠Get opportunities to bring your whole-self to the organization and perform to your best.
Be part of RAKBANK's future! Step into a role where your leadership, and strategic insights will directly shape the future of the bank. If you're ready to elevate your career with us, apply now!
Requirements
- ā¢7+ years of experience in analytics, data science or similar function
- ā¢3-4 years of experience in banking analytics
- ā¢Python and SQL experience required
- ā¢Worked on end-to-end ML model deployment
- ā¢Strong conceptual understanding of machine learning algorithms
- ā¢Excellent knowledge of Banking Functional Knowledge (major plus)
- ā¢Good written, oral communication, documentation skills
Nice to Have
- ā¢SAS/R/Spark as plus
- ā¢Deployment experience (various databases, server/cloud environment: AWS, Azure, and APIs, ODBCs, web apps)
- ā¢Diverse workforce and inclusive culture
Responsibilities
- ā¢Translate business problems into analytical use-cases
- ā¢Develop, implement and test algorithms
- ā¢Collaborate with Business Analysts for requirements
- ā¢Work with Data Engineers to build data pipelines
- ā¢Automate/productionize complex ML models
- ā¢Ensure high coding standards and design standards
- ā¢Drive innovation and enhance existing solutions
- ā¢Deliver end-to-end AI-ML models from development to deployment
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