Bhavana Reddy Bajjuri

• Compiled, analyzed, and reported sales data to provide insights that informed business decisions.
• Monitored and analyzed competitive activity, customer behaviors, and market trends to support strategic planning.
• Provided actionable insights to guide sales and marketing teams in optimizing performance and reaching business objectives.
• Contributed to the development of sales plans and objectives, aligning with overall business goals.
• Collaborated with external and internal clients on various projects, ensuring alignment with business requirements.
• Analyzed customer data to determine trends in purchasing behavior.

Additional Responsibilities:

• Requirement Gathering: Worked closely with stakeholders to understand business needs and documented requirements for data-driven solutions.
• Data Analysis & Reporting: Conducted detailed analyses of business processes, identified improvement opportunities, and provided comprehensive reports to senior management.
• Process Improvement: Identified inefficiencies in sales processes and recommended improvements using data insights and industry best practices.
• Performance Monitoring: Tracked key performance indicators (KPIs) and created dashboards to monitor business performance and support decision-making.
• Solution Design: Assisted in designing and implementing business solutions by translating business needs into actionable functional specifications for technical teams.
• Stakeholder Communication: Communicated analysis findings and project updates to non-technical stakeholders to ensure clear understanding and smooth project execution.

• Designed and implemented machine learning systems and self-running artificial intelligence (AI) models to automate predictive tasks and improve decision-making processes.
• Transformed data science prototypes into scalable production-ready solutions, applying appropriate machine learning algorithms and tools based on project requirements.
• Developed, trained, and deployed models to ensure high accuracy in predicting outcomes and generating recommendations for users.
• Collaborated with cross-functional teams to define requirements, select data sources, and design machine learning workflows that aligned with business goals.
• Monitored model performance and fine-tuned algorithms to maintain accuracy, efficiency, and scalability in real-world environments.
• Integrated machine learning solutions into production systems, ensuring seamless deployment, testing, and monitoring of models.
• Conducted data preprocessing (cleaning, normalization, feature selection) and feature engineering to enhance model performance and optimize prediction accuracy.

Key Projects:

• Computational Data Science Post Graduate Program: Successfully completed, gaining expertise in advanced machine learning and data science techniques.
• Machine Learning Hackathon (Insurance Claim Spam Detection): Developed and deployed a robust model to identify and filter spam claims, improving process efficiency.
• Fish Cost Prediction Model: Built a predictive algorithm to accurately estimate fish costs based on key features, driving operational decision-making.
• Credit Card Fraud Detection: Created a machine learning model that flagged fraudulent transactions with high precision, enhancing security measures for financial institutions.
• Customer Churn Prediction: Designed a predictive model to identify customers at risk of leaving, enabling the business to take proactive retention measures.
• Sentiment Analysis Using NLP: Developed a natural language processing (NLP) model to analyze customer reviews and feedback, generating insights for product and service improvements.
• Collaborative Filtering for User Recommendations: Designed a recommendation engine to enhance user experience by providing personalized suggestions based on historical data.

Additional Responsibilities:

• Explored and implemented cutting-edge algorithms such as decision trees, random forests, gradient boosting, and neural networks, ensuring model robustness.
• Collaborated with data engineers to build efficient data pipelines for continuous integration and deployment (CI/CD) of machine learning models.
• Performed A/B testing and model validation to ensure the effectiveness of deployed models in live environments.
• Documented workflows and findings, preparing reports for stakeholders and making data-driven recommendations for future improvements.