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Tier-1 brand, metro locations, and a mid-level ML/data science role with broad skills increase competition.
Requires specialized ML/AI expertise plus sales-domain context, moderately limiting cross-industry transferability.
Explicit 2–3 years requirement and mandatory ML, NLP, Python, cloud, and production skills create strict filters.
Lead design, development, and deployment of ML/AI models including machine learning, NLP, and generative AI to solve sales and client engagement business problems.
Deliver actionable analytics and insights into sales and client behavior supporting forecasting, incentive refinement, anomaly detection, and strategic decision making.
Collaborate cross-functionally to build scalable data pipelines, operationalize solutions in cloud environments, and communicate performance metrics and insights to executive leadership.
2–3 years of relevant work experience in data science, analytics, or related quantitative field.
Bachelor’s degree in a quantitative discipline such as Computer Science, Statistics, Mathematics, Engineering, or Economics.
Proficiency in Python and SQL; experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and familiarity with NLP techniques.
Experience with cloud-based data platforms (e.g., GCP, AWS, Azure) and data visualization tools (e.g., Tableau, matplotlib) to deliver insights.
Experience working in fast-paced, business-facing roles that require translating complex data into business-relevant insights impacting sales strategy and growth.
Proven ability to operationalize machine learning and AI models in scalable cloud environments with cross-functional stakeholders including Sales, Marketing, and Technology.
Strong technical expertise in modern ML frameworks and generative AI methods combined with effective communication skills for technical and executive audiences.