Jaya Sinha

CITI Bank Project :

• Automate End User Computing (EUC) processes using Knime and Python to streamline tasks involving Excel, XML, or CSV as input files referring to FRD/BRD. Improve efficiency and accuracy, allowing users to react promptly to changing market conditions or regulations, aligning with stakeholder requirements.
• Contributed to different development lifecycle phases, starting with comprehensive requirement gathering and directly connecting to the user/ stadholder diligently documenting requirements in the FRD (Functional Requirement Document). Executed development tasks and rigorously tested workflows against provided sample outputs. Proficiently handled the deployment of knime workflow onto servers, ensuring seamless integration.
• Work closely with stakeholders across various departments and implement optimal communication mechanisms based on stakeholder needs.
• As a POD Lead, I ensured timely delivery of End User Computing (EUC) projects, provided technical guidance, and efficiently managed team leave schedules to maintain project continuity.
• Orchestrated team events, facilitated training for new members, recognized top performers, and provided mentorship in data analysis and Python, fostering a culture of continuous learning and data-driven decision-making.

• Classified products based on their potential hazardous risks, for regulatory storage, safety and compliance.
• Investigated the product bank which has non-eligible products which are missed by ML.
• Labelled products [true positive/false positive] as per the compliance and laws, and fed the same to the ML model.
• Labelled the error data from the ML model for better accuracy.

• I was part of a container freight price prediction project using a Time series model. Cleaned and preprocessed the data for the Ml model.
• Developed polished visualizations to share the results of data analyses.
• Research on container freight price prediction to identify new features which can help improve the accuracy of the model.
• Used pandas and NumPy.
• Made a predictor map for the project.

• Analyzed key aspects of refund to evaluate factors driving results and summarized them into presentations.
• I have to contact the customer and collect data on why the customer has requested a refund.
• Performed EDA on the data set using pandas and NumPy. Suggested improvement plan based on the refund case. On the product, Logistic and SME session.
• Need to give feedback to those who have more than the average number of refunds in the team.
• Built a simple Logistic regression model of fraud Sales based on data trends.

• Analyzed key aspects of refund to evaluate factors driving results and summarized them into presentations.
• I have to contact the customer and collect data on why the customer has requested a refund.
• Performed EDA on the data set using pandas and NumPy. Suggested improvement plan based on the refund case. On the product, Logistic and SME session.
• Need to give feedback to those who have more than the average number of refunds in the team.
• Built a simple Logistic regression model of fraud Sales based on data trends.

• Support international clients(British Telecom) via call, chat and email. Use to help with technical support and billing queries.