Data Scientist

Designed and developed a hybrid search solution within 3 months on Elasticsearch, leveraging vector search for semantic understanding and BM25 for lexical precision;

Integrated a Large Language Model (LLM) to refine results, ensuring the top-ranked items were not just relevant but also the most contextually appropriate for the user's query;

Engineered a custom ranking framework to seamlessly blend search scores with key business metrics like product popularity, availability, and promotional status, creating a system that was both intelligent and commercially effective;

Delivered a high-performance, scalable search platform that significantly improved the customer experience by providing faster, more intuitive, and highly relevant results.

Solved complex business challenges by applying innovative AI/ML approaches, collaborating with cross-functional teams;

Communicated data-driven actionable insights to stakeholders across North America (NA), Latin America (LATAM), and Asia-Pacific (APAC) regions;

Managed end-to-end implementation of solutions, starting from SQL-based data preparation to building AI/ML models using Python;

Deployed AI/ML solutions on cloud-based platforms, ensuring smooth integration and operational efficiency;

Worked closely with stakeholders to understand business requirements and translate them into effective AI/ML solutions;

Ensured scalability and robustness of the AI/ML models deployed on cloud platforms for long-term value;

Led project management tasks, coordinating timelines and deliverables across diverse teams and regions.

Produced performance reports for 15 ML models, driving over $10M in annual benefits by extracting actionable insights from crucial KPIs using SAS, and guiding business decisions on future strategies;

Led a team of three junior consultants in developing an automated model monitoring framework, streamlining the oversight of hundreds of models and reducing manual labor by approximately 90% through seamless integration of industry-standard tools such as Python, PySpark, SQL, and Tableau;

Automated and streamlined the AI/ML model inference process, achieving an 85% reduction in man-hours, significantly improving operational efficiency.

Successfully increased the profit margin by 2% and annual cost savings of ~$15M by optimizing bidding decisions of mortgage loans using a sophisticated machine learning framework in Python;

Demonstrated a strong aptitude for data analysis and problem-solving resulting in a full-time offer.