Ramana lalam

As a Data Scientist, I am driven by a determined ambition to achieve mastery in complex problem-solving. My approach is deeply rooted in logic and a strong sense of accountability, ensuring I deliver precise, pragmatic solutions that consistently drive impactful results.

Experience

A

Data Scientist

ADA GLOBAL · Sep 2023 – Present

Not yet confirmed

• Designed and deployed end-to-end machine learning and GenAI solutions across Databricks and client-managed data platforms for multiple enterprise e-commerce clients, delivering measurable impact across demand forecasting, finance, promotions, and customer analytics.
• Partnered closely with BI, Data Engineering, and client stakeholders to productionize scalable, secure, and reliable data workflows integrated directly into client systems.
• Built a machine learning pipeline to estimate price elasticity for an e-commerce retailer using monthly SKU eligibility checks, automated feature engineering, and data quality validation.
• Automated the entire workflow in Databricks to run monthly and generate SKU-level price elasticity insights.
• Impact: Achieved around 70% accuracy, reduced manual effort, and helped improve pricing decisions to drive better revenue and margins.
• Developed and deployed a deep learning demand forecasting pipeline with automated feature engineering, cost-aware optimization, and Word2Vec product embeddings across Databricks and client ML platforms.
• Impact: Improved forecast accuracy by 20-30% and reduced stockouts by 12-18%.
• Developed a machine learning-driven inventory liquidation planner for an e-commerce retailer to identify products for clearance based on shelf life, Days of Inventory (DOI), expiry, and inventory health.
• Automated the end-to-end pipeline using Databricks to generate periodic liquidation recommendations.
• Impact: Reduced manual planning effort by ~80%, improved inventory turnover by ~20%, and reduced excess inventory carrying costs by ~15%.
• Developed an end-to-end product affinity recommendation pipeline for an e-commerce retailer using the FP-Growth algorithm to identify high-performing product bundles (up to four items) across sellers and campaigns.
• Built automated bi-monthly PySpark pipelines and modular dashboards to generate bundle recommendations and evaluate their effectiveness using metrics such as bundle conversion, repeat purchase rate, and new customer acquisition.
• Impact: Improved bundle conversion by ~15%, and enabled data-driven bundle recommendations to increase cross-sell opportunities and GMV.
• Developed a multi-agent LLM-powered analytics assistant to automate SQL query generation, database execution, root cause analysis, and visualization-based reporting.
• Implemented LangChain-based agents for SQL generation, visualization, and RCA with secure query execution, and built a Streamlit chat interface with inline SQL editing and dynamic charts for interactive data exploration.
• Impact: Reduced dependency on data teams, accelerated insight generation through AI-powered natural language data exploration.

Data Scientist

ADA · Sep 2023 – Present

Not yet confirmed