P

Pranali Waghule

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California, United States

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Résumé

Jobs verified_user 0% verified
  • NetDocuments
    Data Analyst
    NetDocuments
    Jun 2024 - Current (2 years 5 months)
    • Reduced first-year churn by 3% and protected $2.5M in ARR by developing churn analytics in Python (Pandas, Scikit-learn), A/B testing and SQL, integrating 5,000+ NPS responses with Salesforce & Gainsight data. • Designed 10+ Power BI dashboards combining Salesforce, Gainsight, and ZoomInfo data, enabling Product, Revenue Ops & Customer Success teams to proactively identify expansion opportunities and monitor customer health in real time. • Led large-scale Salesforce data migration, extraction, enrichment, and AI-driven cleansing for 204K+ account records, integrating internal systems with Marketo, ZoomInfo, and Revelio for the Sales Analytics team to raise data accuracy from 81% to 97.4% and enhance Salesforce data readiness.
  • U
    Product Manager
    UC San Diego Integrated Procurements & Surplus Services -
    Mar 2024 - Jun 2024 (4 months)
    • Led a four-member Agile team to design a digital surplus-pickup solution integrating an AI image parser and LLM scheduling agent, cutting client email traffic by 80% and increasing daily capacity from 10 to 30jobs, which added an estimated $150K in annual revenue. • Conducted userjourney mapping and stakeholder interviews to define product requirements, backlog prioritization, and sprint goals in JIRA. • Built a log-linear regression staffing model that identified crew size and manager confirmation as main labor drivers, results adopted into scheduling, projected to eliminate 10 hours of weekly overtime and save $60K annually.
  • Infosys Ltd.
    Business Intelligence System Engineer
    Infosys Ltd.
    May 2021 - Jun 2023 (2 years 2 months)
    • Led a 5-member team to migrate 2.5 TB+ of legacy enterprise data using SQL, Python, and Informatica, ensuring data continuity as the legacy platform was sunsetted, resulting in $750 K+ in retained reporting value and operational reliability for a Fortune 500 client. • Automated ETL workflows to streamline multi-source data ingestion and transformation, reducing manual processing by 40% and improving consistency for cross-departmental analytics. • Built 10+ BI dashboards (Power BI, Oracle BI) delivering real-time KPIs, used by 50+ stakeholders across Finance, Operations, and Product, accelerating monthly reporting cycles. • Conducted margin and pricing analyses using SQL and Python (Pandas, NumPy) to identify cost-saving opportunities,
  • I
    Data Analyst Intern
    In.Corp Advisory
    May 2020 - Oct 2020 (6 months)
    • Analyzed multi-channel customer data and identified churn signals; insights informed retention strategies that improved repeat engagement by 2% in pilot campaigns. • Increased consumer acquisition by 30% through Python-based email optimization, leveraging data-driven strategies to enhance performance
Education verified_user 0% verified
  • University of California San Diego
    Master's of Science
    University of California San Diego
    Jul 2023 - Dec 2024 (1 year 6 months)
  • Savitribai Phule Pune University
    Bachelor's of Engineering
    Savitribai Phule Pune University
    Jul 2017 - Oct 2020 (3 years 4 months)
Projects (professional or personal) verified_user 0% verified
  • C
    Customer Churn Prediction (T-Mobile)
    Jan 2024 - Mar 2024 (3 months)
    • Built a logistic regression model in Python to identify churn drivers, integrated SQL queries to extract telecom usage data, and delivered Power BI dashboards to business teams. Projected churn reduction of 2%.
  • C
    Customer Lifetime Value Prediction
    Oct 2023 - Dec 2023 (3 months)
    • Designed Random Forest CLV model that improved targeting efficiency by 20%, increasing conversion rates from 8%→9.6% in test campaigns.mer policy/claims data, improving targeted marketing efficiency by 20%.
  • D
    Data Pipelines (Warehouse Management)
    Aug 2023 - Dec 2023 (5 months)
    • Collaborated with a team of 5 to utilize Python for data extraction and transformation from the Snowflake Database revealing correlations between weather patterns and order trends through various data formats (CSV, XML, PSQL). • Reduced data processing time by 40% by implementing Python multiprocessing and Snowflake query optimization.