Mithula Roy

• Supported data validation and reporting processes aligned with operational standards and compliance.
• Collaborated with cross-functional teams to address data-related queries and operational issues.
• Ensured data accuracy and integrity, supporting quality and compliance requirements.
• Communicated insights and reports to stakeholders in a clear and structured manner.
• Assisted in improving process efficiency and reporting systems.
• Worked in a fast-paced, team-based environment, ensuring timely delivery of outputs.
• Contributed to data pipeline workflows and ETL processes.
• Communicated with stakeholders Communicated with stakeholders and identified process inefficiencies and data
gaps, improving reporting accuracy and reducing turnaround time by 30%.

Executed an intensive internship focused on Artificial Intelligence and Machine Learning, delivering analytical workflows aligned with emerging data-driven methodologies.
Designed and delivered a capstone machine learning project titled “Brain Tumour Segmentation Based on Local Independent Projection-Based Classification, ” achieving measurable model performance outcomes. Enabled hands-on development across a spectrum of machine learning algorithms, facilitating model refinement, feature engineering, and iterative experimentation.
Collaborated with technical stakeholders to integrate advanced techniques for classification, clustering, and predictive modeling.
Demonstrated operational proficiency with analytics libraries such as Python, TensorFlow, and scikit- learn, strengthening analytical throughput and model optimization.
Translated technical findings into concise insights for panel evaluation, showcasing capability to communicate complex analytical concepts to diverse audiences.
Applied statistical reasoning and algorithmic methods to support exploratory data analysis and structured model experimentation.
Supported end-to-end project execution, reinforcing adherence to analytical standards and reproducibility best practices.

• Medical Imaging Project : Developed a Brain Tumour Segmentation model using Python, TensorFlow and Scikit
learn improving classification accuracy through iterative feature engineering.
• Predictive Modeling: Applied advanced clustering and classification algorithms to process complex datasets,
streamlining analytical workflows and model throughput.
• Conducted exploratory data analysis (EDA) to identify patterns and improve model performance.
• Stakeholder Communication: Collaborated with technical stakeholdersto integrate advanced techniquesfor
classification, clustering, and predictive modeling.
• Best Practices: Applied statistical reasoning and algorithmic methodsto support exploratory data analysis and
structured modelexperimentation. product category and marketing
prioritization. operational
strategy decision-making.
planning.