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Tier-1 brand, broad ML/GenAI requirements, and a popular data science title drive high candidate competition.
Core ML/AI skills transfer across industries, though financial domain knowledge provides advantage.
Explicit 2–3 year minimum and mandatory ML, Python, SQL, cloud, and production experience.
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Lead development and implementation of data-driven strategies to improve sales portfolio performance by delivering actionable insights.
Design, develop, and operationalize machine learning, NLP, and generative AI solutions to enhance client engagement and channel efficiency.
Collaborate cross-functionally with Sales, Marketing, Finance, Technology, and Capabilities teams to identify opportunities and prioritize high-impact initiatives with measurable business impact.
2–3 years of relevant work experience in data science, analytics, or a related quantitative field.
Bachelor’s degree in a quantitative discipline (e.g., Computer Science, Statistics, Mathematics, Engineering, Economics).
Proficiency in Python, SQL, and experience applying machine learning techniques using standard libraries (scikit-learn, TensorFlow, PyTorch).
Experience working with cloud-based data platforms (GCP, AWS, or Azure) and data visualization tools; strong analytical problem-solving skills.
Experienced in translating complex business problems into quantitative models and communicating findings clearly to diverse stakeholders.
Comfortable operating in a fast-paced, collaborative, business-facing environment with measurable impact goals.
Demonstrated ability to develop and deploy ML/AI solutions including NLP and generative AI techniques to solve real-world business challenges.