int(6759)
Mumbai, India

AVP Business Intelligence

Our client is a leading global financial services firm which operates across four business segments and having operations across 60 countries. They are looking to hire a senior professional with data analytics and business intelligence skills.

This position is an exciting opportunity to join the Group’s Asset Management Business and provide analytical support to Sales and Marketing teams within the Asset & Wealth Management business.

Please contact Anamika Bhattacharjee or email your cv directly in word format with job reference number: JO0000005200 to banking-India@theedgepartnership.com

Please note that due to the high number of applications only shortlisted candidates will be contacted. If you do not hear from us in the next 5 business days we regret to inform you that your application for this position was unsuccessful.

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Key responsibilities

  • Partnering with distribution stakeholders to identify opportunities for leveraging advanced analytics
  • Embed analytics process in organizational decision making: descriptive, diagnostic and predictive analytics to drive better business outcomes for sales and marketing teams
  • Building analytical frameworks for marketing measurement – A/B testing, campaign ROI, cost of acquisition modeling, lifetime value, etc.
  • In-depth data exploration and data mining to develop a deep understanding of client behavior
  • Leveraging machine learning models / techniques to target opportunities and optimize processes and developing tools / solutions to measure model performance over time.

Role requirements

  • MBA or advanced degree in Statistics, Math, Engineering or other quantitative-focused field preferred
  • Experience in an analytics role, financial services/marketing preferred
  • Experience using computer languages (Python, Pyspark, Google Analytics, etc.) to manipulate and analyze large datasets
  • Experience using machine learning libraries in Python (Sci-kit learn, tensorflow, etc.)
  • Knowledge of variety of machine learning algorithms (linear regression, logistic regression, SVMs, Tree-based models, neural networks, etc.) and techniques (cross validation, feature selection approaches, hyper parameter optimization, missing data imputation etc.)
  • Strong problem-solving skills