Role Overview:

As a Lead AIML Engineer specializing in Classification Modeling, you will spearhead the development of advanced predictive engines that drive decision-making across the CPG, FMCG, and Retail sectors. You will work at the intersection of complex data architecture and business strategy, collaborating closely with cross-functional stakeholders, product managers, and data engineering teams to translate ambiguous business challenges into scalable machine learning solutions. By architecting high-impact classification models, you will directly influence customer acquisition, churn prediction, and inventory optimization, ultimately shaping the bottom-line performance of global retail brands. This role demands a blend of technical rigor and strategic foresight to deliver actionable insights that empower business leaders to navigate competitive market landscapes.

Key Responsibilities:

- Design and deploy robust classification models to solve complex retail challenges, such as customer segmentation and purchase propensity, ensuring high predictive accuracy and business relevance.

- Lead the end-to-end lifecycle of machine learning projects, from data ingestion and feature engineering to model deployment and performance monitoring, to ensure seamless integration into production environments.

- Partner with marketing and supply chain stakeholders to translate business requirements into technical roadmaps, ensuring that data-driven insights align with organizational growth objectives.

- Mentor junior data scientists and engineers by fostering a culture of technical excellence, code quality, and continuous learning within the analytics team.

- Optimize existing predictive frameworks to improve computational efficiency and model interpretability, providing stakeholders with clear, actionable intelligence for strategic planning.

Required Skillset:

- Demonstrated expertise in building and scaling classification models using Python, with a deep understanding of statistical modeling techniques and their application in retail and marketing analytics.

- Proven ability to communicate complex technical findings to non-technical stakeholders, bridging the gap between data science outputs and business strategy.

- Strong leadership capabilities, with a history of guiding high-performing teams through the complexities of large-scale data projects in a fast-paced environment.

- Exceptional analytical problem-solving skills, with the ability to navigate large, unstructured datasets to uncover hidden patterns that drive revenue and operational efficiency.

- A Master's degree or higher in Computer Science, Statistics, Mathematics, or a related quantitative field is preferred, reflecting a strong foundation in theoretical and applied data science.

- Flexibility to thrive in a hybrid work environment across Chennai, Hyderabad, or Bangalore, maintaining high levels of productivity and collaboration across distributed teams.

- A minimum of 8 to 11 years of professional experience in the data science domain, specifically within the CPG, FMCG, or Retail industries.