Sree Harsha Mailapalli
Professional Summary
AI Business Analyst has experience translating complex data into actions that improve customer retention, investment decisions, and sales performance. Strong in Python, SQL, Tableau, and advanced modeling with XGBoost and scikit-learn. Have built real-time churn dashboards for SaaS, created predictive and clustering analyses for venture-capital diligence in health-tech, and driven margin optimisation for HVAC sales. Comfortable working in Jupyter Notebook, Google Colab, and Git, and skilled at presenting insights to finance, customer-success, and executive stakeholders. Proven record of reducing model deployment cycles, shortening proposal lead times, and shaping data-driven playbooks that guide strategic decisions. Seeking to apply technical depth and business acumen to deliver high-impact analytics solutions in fast-moving environments.
Technical Skills
Work Experience
- Diagnosed and fixed a production bug that blocked Impact Hub's core task-submission workflow , restoring users' ability to submit work, then extended it into a full pending/accepted/rejected status-tracking system with clear user feedback
- Designed and built an in-app messaging feature that let contributors communicate directly with project owners, so questions no longer had to happen outside the platform
- Shipped platform-wide UX improvements, including a unified notification system, a guest onboarding experience for new visitors, dark/light theme support, and UI fixes across the application
- Conducted a full application security audit and documented 5 findings, each with a root-cause explanation and a concrete remediation plan
- Built a self-updating technical documentation system that auto-generates reference material from the live codebase, keeping docs in sync with the code and supporting a smooth project handover
- Prototyped ReferralPulse, a referral and engagement analytics app, with a multi-tier referral tree, a commission payout ledger, and customer engagement charts
- Built a customer churn analytics dashboard with summary metrics for at-risk customers and revenue at risk, a sortable customer table with color-coded risk badges, and a risk distribution chart
- Developed a blockchain-based data integrity prototype in Python, including a 2-node network simulation that showed read-only replication and verification, as groundwork for an AI memory R&D initiative
- Consolidated disparate startup datasets inside Jupyter Notebooks using Python Pandas and SQL, reducing preprocessing time by 40 percent.
- Performed exploratory analysis with Seaborn to uncover market-sizing patterns that informed investment theses for mental-health ventures.
- Built k-means clustering in scikit-learn that segmented user behaviour and highlighted distinct customer cohorts for product–market-fit assessment.
- Developed logistic regression scorecards to benchmark retention potential, enabling partners to compare prospects on a standardised engagement scale.
- Summarised statistical findings into two-page investment briefs that allowed partners to reach go/no-go decisions within 48 hours.
- Facilitated weekly review sessions with cross-functional interns to refine model assumptions and align analytics outputs with partner questions.
- Analyzed historical project records in Excel to reveal a 12 percent margin gap between quoted and realised costs, triggering a repricing initiative.
- Maintained SQL-backed CRM dashboards that surfaced a live pipeline value of ₹85 million, giving leadership daily revenue visibility.
- Automated quote-generation spreadsheets with Excel macros, cutting proposal turnaround time from 3 days to 1 day.
- Standardized data-capture templates across pre-sales, engineering, and procurement teams to eliminate downstream specification mismatches.
- Built Tableau dashboards that ranked failure modes by frequency, equipping design engineers with prioritised remediation actions.
- Documented customer interactions and pricing decisions in a searchable knowledge base to accelerate onboarding for new sales staff.
- Analyzed and validated data using SQL queries to identify inconsistencies, verify application functionality, and support data-driven testing decisions.
- Gathered and interpreted functional requirements, executed manual test cases, and documented defects to ensure system outputs aligned with business requirements.
- Collaborated with cross-functional teams throughout the SDLC to analyze requirements, communicate findings, and support process and application improvements.
- Worked in an Agile environment, contributing to requirement discussions, testing activities, issue tracking, and documentation across project lifecycles.
- Preparing, validating and analyzing business datasets in supervised analytics projects, and turning trends and key performance indicators into findings for business audiences.
- • Developing dashboards and visualizations and reviewing analytical outputs for accuracy, completeness and consistency, with findings summarized in reports and presentations.
Education
Certifications
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