RUSHIKESH POKALE

• Contributed to developing an XGBoost-based recommendation system on AWS, improving cross-sell by 15% and retention by 10%. • Supported NLP feedback analysis using Hugging Face Transformers, cutting manual review time by 60%. • Built deep learning models (TensorFlow/Keras) for real-time parking detection, achieving 83% accuracy. • Assisted in streamlining ML pipelines using MLflow & Docker, helping reduce deployment time by 40%. • Helped automate monitoring via Datadog, enabling real-time anomaly detection and minimizing system downtime.

• Contributed to developing an XGBoost-based recommendation system on AWS, improving cross-sell by 15% and retention by 10%. • Supported NLP feedback analysis using Hugging Face Transformers, cutting manual review time by 60%. • Built deep learning models (TensorFlow/Keras) for real-time parking detection, achieving 83% accuracy. • Assisted in streamlining ML pipelines using MLflow & Docker, helping reduce deployment time by 40%. • Helped automate monitoring via Datadog, enabling real-time anomaly detection and minimizing system downtime.

• Contributed to developing an XGBoost-based recommendation system on AWS, improving cross-sell by 15% and retention by 10%. • Supported NLP feedback analysis using Hugging Face Transformers, cutting manual review time by 60%. • Built deep learning models (TensorFlow/Keras) for real-time parking detection, achieving 83% accuracy. • Assisted in streamlining ML pipelines using MLflow & Docker, helping reduce deployment time by 40%. • Helped automate monitoring via Datadog, enabling real-time anomaly detection and minimizing system downtime.

• Created Tableau dashboards and automated SQL reports, reducing reporting time by 35%. • Helped design and optimize ETL pipelines, improving data ingestion efficiency by 40%. • Supported risk forecasting using statistical models, improving accuracy by 20%. • Collaborated with business teams to define KPIs, supporting segmentation efforts and reducing churn by 15%. • Conducted deep-dive analyses to uncover cost-saving opportunities and improve operational workflows by 20%.

• Created Tableau dashboards and automated SQL reports, reducing reporting time by 35%. • Helped design and optimize ETL pipelines, improving data ingestion efficiency by 40%. • Supported risk forecasting using statistical models, improving accuracy by 20%. • Collaborated with business teams to define KPIs, supporting segmentation efforts and reducing churn by 15%. • Conducted deep-dive analyses to uncover cost-saving opportunities and improve operational workflows by 20%.

• Created Tableau dashboards and automated SQL reports, reducing reporting time by 35%. • Helped design and optimize ETL pipelines, improving data ingestion efficiency by 40%. • Supported risk forecasting using statistical models, improving accuracy by 20%. • Collaborated with business teams to define KPIs, supporting segmentation efforts and reducing churn by 15%. • Conducted deep-dive analyses to uncover cost-saving opportunities and improve operational workflows by 20%.

• Developed a predictive model to optimize resource allocation, boosting scheduling efficiency by 15%. • Analyzed and cleaned large datasets using Python and SQL, reducing manual reporting time by 5 hours per week.

• Developed a predictive model to optimize resource allocation, boosting scheduling efficiency by 15%. • Analyzed and cleaned large datasets using Python and SQL, reducing manual reporting time by 5 hours per week.

• Developed a predictive model to optimize resource allocation, boosting scheduling efficiency by 15%. • Analyzed and cleaned large datasets using Python and SQL, reducing manual reporting time by 5 hours per week.

• Developed predictive models to enhance customer retention by 10% and reduce churn through targeted insights. • Created and presented data visualizations using Python and Tableau, effectively communicating findings to stakeholders.

• Developed predictive models to enhance customer retention by 10% and reduce churn through targeted insights. • Created and presented data visualizations using Python and Tableau, effectively communicating findings to stakeholders.

• Developed predictive models to enhance customer retention by 10% and reduce churn through targeted insights. • Created and presented data visualizations using Python and Tableau, effectively communicating findings to stakeholders.

• Conducted market research and data analysis to identify customer trends for a marketing campaign. • Optimized outreach strategies for improved engagement and managed social media accounts. • Leveraged CRM software and content creation to support lead generation, resulting in a 15% increase in sales inquiries.

• Conducted market research and data analysis to identify customer trends for a marketing campaign. • Optimized outreach strategies for improved engagement and managed social media accounts. • Leveraged CRM software and content creation to support lead generation, resulting in a 15% increase in sales inquiries.