Bias and Fairness in DataAnalysis of data to find bias in historical data using explainable AI, machine learning models based on XGBoost, ensemble learning and APIs like IBM AIF360, which are treated in the stages of pre-processing, in-processing and post-processing through which bias is potentially mitigated by employing various statistical metrics like Adversarial Debiasing, re-weighing etc. Further model explains were done using SHAP (SHapley Additive exPlanations)  and LIME( local interpretable model-agnostic explanations) to find the features responsible for ML predictions and potential bias.

My MSc Data Science programme equipped me with advanced skills in data analysis, machine learning, and cloud computing. Through rigorous coursework and hands-on projects, I gained proficiency in Python, SQL, and various data science libraries like pandas, NumPy, and TensorFlow. I also developed expertise in ETL/ELT pipelines, model deployment on cloud platforms like Azure and AWS, and data visualization techniques using tools like Plotly and Seaborn. Additionally, I honed my problem-solving abilities and learned to extract actionable insights from complex datasets. Overall, the programme provided a comprehensive foundation in data science, enabling me to tackle real-world challenges and drive innovation in diverse industries.During my MSc, I delved into research focusing on bias and fairness in data, employing cutting-edge tools such as AIF360, SHAP, and LIME. Through this exploration, I gained insights into mitigating biases and ensuring fairness in machine learning models, crucial for ethical and equitable decision-making processes. Additionally, I conducted research on disease transmission stochastic modelling, utilizing SEIR and SIR models. By developing and analysing these models, I deepened my understanding of epidemiological dynamics, contributing to the understanding and prediction of disease spread and informing public health interventions.

During my 6 tenure at JPMorgan Chase, I served as an Associate Software Engineer(Python and Java), playing a pivotal role in diverse projects spanning parallel computing, RESTful microservices, and cloud computing integration. I led the development of ETL/ELT pipelines and RESTful microservices, leveraging technologies like Tornado and Django. Notably, I spearheaded initiatives to enhance system performance, implementing caching layers and enabling Continuous Integration and Development (CI/CD) methodologies, resulting in a 70% performance improvement. Additionally, I championed the integration of OAuth2 and Single Sign-On (SSO) for Docker container-based platforms, ensuring robust security measures. My contributions underscored a commitment to excellence and innovation in software engineering.