Project Management Business Analyst

ALBERTO AKKARAKKARAN

Contact: +1-860-***-**** Email: **********@*****.*** LinkedIn: link

EDUCATION

University of Connecticut, Hartford, CT Expected May 2026 Master of Science (M.S.) in business analytics and project management

• Relevant Coursework: Project management, Predictive Modelling, Statistics, Python, R Programming, SQL, Data Mining, Time Series Forecasting. Jira, Agile methodology. SKILLS

Programming Languages: Python (Pandas, NumPy, Matplotlib, Scikit-learn) Statistical Packages: SQL, R Programming, SAS, Oracle MariaDB, JMP Pro Data Visualization Tool: Power BI, Tableau

Microsoft Office: Advance Excel (VBA, Pivot Tables), PowerPoint, Word Project Management: Agile, JIRA, MS Project, Risk Mitigation, Cross-functional Collaboration WORK EXPERIENCE

Program Manager – Business Analyst

MDS Coating (Manufacturer for John Deere) May 2023 – July 2024

• Designed and developed 15 interactive Tableau dashboards and SQL-based data models that enhanced supplier performance visibility, resulting in a 20% increase in logistics efficiency across multiple projects.

• Conducted comprehensive root cause analyses on supply chain delays affecting over 50 suppliers, implementing targeted strategies that successfully reduced lead times by an average of 15%, improving overall operational reliability.

• Collaborated with cross-functional teams to deliver actionable insights through compelling data storytelling presentations, earning recognition and praise from senior management for increasing stakeholder engagement by over 30%

Business Analyst Intern

Avanthi Motor Works Jan 2023 – May 2023

• Automated data aggregation processes utilizing SQL and Excel, achieving a 20% reduction in reporting time, which enabled timely decision-making across teams and improved overall project efficiency by 25%.

• Developed dynamic Power BI dashboards to analyze dealership performance metrics, providing insights that led to a 15% increase in sales through the identification of trends in revenue and customer demographics.

• Conducted comprehensive exploratory data analysis (EDA) to pinpoint crucial key performance indicators (KPIs), enhancing sales forecasting accuracy by 30%, thereby allowing for more strategic inventory management. RELEVANT PROJECTS

Web Traffic: Predictive Insights for Smarter Site Strategy (Github) Dec 2022 – Jan 2023

• Designed Conducted a comprehensive analysis of forecasting models, identifying the SARIMA (3,0,1) (1,1,0) as the most accurate with an AIC of 142 and SBC of 145, leading to a 30% improvement in prediction accuracy for page load times compared to previous models.

• Developed and executed strategic initiatives based on traffic trend forecasts that resulted in a 25% increase in user engagement during peak periods by optimizing infrastructure scaling and enhancing content release timing.

• Established a systematic data-driven decision framework that streamlined weekly data aggregation processes by 40%, facilitated model development through advanced decomposition analysis techniques, and automated metric evaluations via Excel macros to ensure efficient future forecasting tasks. Employee Attrition Classification Data JMP (Github) Aug 2024 - Dec 2024

• Developed a predictive model analyzing over 15,000 employee records to identify key factors influencing job satisfaction and overtime, resulting in a targeted strategy that reduced attrition rates by 20% within six months.

• Achieved an AUC of 0.7419 using a Boosted Tree model for employee attrition prediction, significantly enhancing forecasting accuracy compared to traditional Decision Tree methods and increasing the effectiveness of HR interventions by 35%.

• Created innovative metrics including the Burnout Indicator and Salary Competitiveness Index, facilitating actionable retention strategies that improved employee satisfaction scores by 15% and decreased turnover costs. Credit Card Customer and Transaction Analysis (Github) Dec 2022 – Jan 2023

• Developed an interactive dashboard that synthesized data from over 10,000 credit card customers, resulting in a 30% improvement in reporting efficiency and enabling stakeholders to make informed decisions based on real-time insights.

• Executed complex data transformations using Power Query and DAX to segment customers by age and income, yielding a targeted marketing strategy that increased engagement rates by 40% across key demographics.

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