Hello, I'm Akshit—a passionate tech enthusiast and data aficionado who believes in the transformative power of numbers to tell compelling stories and drive informed decisions. But there's more to me than just code and algorithms. I'm a curious explorer at heart, always eager to uncover new learning opportunities. Whether it's delving into the latest machine learning trends or mastering the newest tools in data visualization, I'm there, ready to dive in. I thrive in diverse environments and strongly believe that the best solutions emerge from collaborative efforts. Bringing people together to solve complex problems is something I truly enjoy.
Surge IT , Business Intelligence Analyst - Virginia (Sept 2024 – present)
• Led Agile ceremonies with Software and product teams to identify risks, mitigate issues, and track active sprint outcomes.
• Facilitated requirement gathering, managed Scrum projects in JIRA, conducted GAP analysis, and validated QA releases.
• Developed Tableau dashboard analyzing financial data, increasing sales by 15%, revenue by 10%, and retention by 20%.
University of South Florida, Graduate Analyst - Information Technology, Florida (August 2022 – May 2024)
• Optimized JIRA workflows and task prioritization, improving issue resolution by 50% and satisfaction by 25%.
• Maintained university website, resolving bugs, and boosted student engagement by 50% with UI/UX improvements.
• Managed SQL databases, executing MySQL queries for data extraction, manipulation, and performance optimization.
• Prepared System Requirements Specifications and Business Requirements Documents, reducing ambiguities by 40%.
Ultimate Kronos Group (UKG), Business Analyst – Product Development, India (Jan 2022 – Aug 2022)
• Led Agile methodologies, Scrum ceremonies, and UAT for 100% successful product delivery and stakeholder satisfaction.
• Facilitated requirements gathering, documented BRD/SRD/User stories, improving cross-functional team communication.
• Developed test plans in Confluence, coordinated QA testing and led JIRA defect triage meetings to resolve 87 critical issues.
• Directed quality assurance principles in an Agile environment, including test planning, execution, and defect management.
• Streamlined JIRA, resolved 70% of issues, and enhanced cross-collaboration by 25% between the product manager& QA.
• Collaborate with development and product teams to prioritize backlogs and define user stories and acceptance criteria.
• Identified product pain points by Salesforce, providing insights to boost customer satisfaction and retention rates by 40%.
• Presented sprint deliverables, user experience insights, and product performance reports to management and leaders.
At the University of South Florida excelling in master of science in Business Analytics and Information Systems, I have engaged in a variety of projects that have sharpened my skills in Business Intelligence and Data Analysis. Leveraging tools such as Tableau, Natural Language Inference (NLI), Machine Learning, and Python, I have tackled real-world challenges. My projects range from analyzing U.S. recessions to exploring multilingual text relationships through NLI frameworks, and building predictive models for public health concerns like monkeypox and abnormal heart rates. This academic journey has not only enhanced my technical expertise but also deepened my passion for collaborative problem-solving.
Developed a Natural Language Inference (NLI) model focused on classifying logical relationships in sentence pairs based on premise and hypothesis from text in fifteen different languages.
Learn moreThis project analysis U.S. recessions and employment dynamics, identifying key triggers and assessing their impact on industries and states. Through a detailed examination of economic indicators, such as GDP fluctuations and unemployment rates, the goal is to provide nuanced insights into how different sectors and regions respond during economic downturns.
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Implemented advanced ML models (Cross-Sectional Neural Networks, LSTM, Deep LSTM, GRU) to classify 9,026 heartbeat measurements, achieving an impressive 94.14% test accuracy.
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Analyzed Monkeypox dataset using ML algorithms (K-NN, Decision Tree, Logistic Regression, ADA Boost, Gradient Descent).
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Through this case study we analyse the failure of Blackberry and provide solutions to maintain its position in the market which can be traced to a number of factors, including their lack of financial stability, their inability to innovate to expand its customer base, the functional limitations of their operating systems, their industry competitions, among many others. This case study can serve as a lesson for tech companies that become complacent after experiencing success.
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The article highlights the issue of algorithmic bias and presents practical solutions to minimize its impact. This is a crucial issue for the future because AI is becoming more and more widespread in various fields, such as hiring, criminal justice, and social media. (Designing AI for All, p. 1) It is vital to understand and reduce algorithmic bias to ensure that AI systems are just unbiased, and responsible. Ultimately, this will create a future where technology benefits everyone and does not perpetuate societal biases.
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