Specialized Analytics Manager

• Leading a team of data scientists and analysts responsible for the full lifecycle of machine learning model development and deployment
• Working on multiple data science projects throughout the year on business problems across the client life cycle – acquisition, engagement, client experience, and retention – for the TTS business
• Understanding business needs, designing, developing, and deploying machine learning models, and communicating insights and recommendations to stakeholders
• Leveraging multiple analytical approaches, tools, and techniques, working on multiple data sources to provide data-driven insights and machine learning solutions to business and functional stakeholders

• Bachelor’s Degree with 7-10 years of experience in data analytics, or Master’s Degree with 6-10 years of experience in data analytics, or PhD
• Marketing analytics experience
• Experience on business problems around sales/marketing strategy optimization, pricing optimization, client experience, cross-sell and retention
• Experience across different analytical methods like hypothesis testing, segmentation, time series forecasting, test vs. control comparison
• Strong hands-on knowledge of Data Science and Machine Learning, including supervised learning algorithms (both Classification and Regression) such as Linear Regression, Random Forest, XGBoost, Support Vector Machines, as well as unsupervised learning techniques (e.g., clustering, dimensionality reduction)
• Experience building and deploying time series models for forecasting and anomaly detection
• Experience with unstructured data analysis, e.g., call transcripts, using Natural Language Processing (NLP)/Text Mining techniques
• Experience building end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment
• Proficient in Python/R, PySpark, SQL
• Proficient with ML libraries such as Scikit-learn, TensorFlow and PyTorch
• Proficient in MS Excel, PowerPoint

• Experience in financial services
• Deep learning experience
• able to work with Neural Networks using TensorFlow and/or PyTorch
• Knowledge on working with GenAI and LLM
• Experience with Tableau
• Experience in Hive

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