Data Scientist - Data Engineer with Analytics Impact

NIHAR DUGADE

New York, NY Open to relocation +1-551-***-**** Email LinkedIn Github

EDUCATION

Master of Science, Data Science Stevens Institute of Technology, Hoboken May 2025 Bachelor of Science, Physics DG Ruparel College, Mumbai, India June 2022 WORK EXPERIENCE

Just Innovation Fund LLC Remote, NY

Data Analyst Oct 2025 – Present

● Built integrated SQL and Python data pipelines combining product, finance, and user behavior datasets, reducing reporting latency by 35% and enabling real-time KPI tracking

● Engineered automated data validation and anomaly detection frameworks, reducing KPI inconsistencies by 22% and improving executive dashboard accuracy and trust

● Designed dimensional data models (fact and dimension tables) to support cohort, retention, and funnel analysis, improving query performance and enabling scalable self-service analytics

● Delivered actionable insights through deep-dive analysis using SQL, Python, and Excel, identifying user behavior trends that informed product optimization and business strategy decisions

● Developed interactive Tableau dashboards tracking revenue, retention, and conversion funnels, enabling stakeholders to monitor business performance and prioritize high-impact initiatives

● Performed funnel and cohort analysis to identify user drop-off points, contributing to targeted product improvements and measurable increases in user engagement

Cantonica Remote, NY

Data Engineer Intern Jan 2025 – May 2025

● Monitored and validated high-volume Kafka and MongoDB data pipelines, ensuring data completeness and reliability for downstream analytics and reporting workflows

● Maintained real-time Spark Streaming pipelines with checkpointing and recovery mechanisms, achieving 99.9% uptime and ensuring consistent data availability for analytics teams

● Optimized large-scale data transformations using Spark, reducing processing latency and improving accessibility of analytics-ready datasets for business use cases

● Automated deployment workflows using Docker and CI/CD pipelines, reducing release cycles by 30% and improving overall pipeline reliability and engineering efficiency

● Collaborated with analytics stakeholders to translate reporting requirements into structured datasets, enabling accurate and consistent dashboard insights

LRA Packaging Remote, India

Data Analyst Jul 2021 – Sep 2022

● Analyzed operational and IoT datasets using SQL and Python to identify machine failure patterns, reducing operational downtime by 20% and improving production efficiency

● Built scalable ETL pipelines using AWS (Glue, EMR, S3), reducing data latency by 40% and enabling near real-time analytics for business decision-making

● Designed star schema data models in Redshift, improving query performance and reducing reporting turnaround time by 35%

● Developed Excel-based reporting solutions, enabling non-technical stakeholders to perform self service data analysis

● Implemented data validation and governance frameworks, improving data consistency by 30% and increasing stakeholder confidence in reporting outputs

SKILLS

Languages: Python, SQL (Advanced), R

Data Analysis & Analytics: Data Cleaning, Exploratory Data Analysis, KPI Development, Data Validation, Statistical Analysis, A/B Testing

Data Visualization & BI: Tableau, Dashboard Development, Data Storytelling, Reporting & Insights Data Management & Warehousing: Data Modeling ( Snowflake), Data Warehousing (Redshift), Data Integration, ETL/ELT Tools & Technologies: Excel (Advanced: Pivot Tables, VLOOKUP), Apache Spark, Kafka, AWS (S3, Glue, Lambda, Athena) Workflow and Tools : Git, CI/CD, Docker, Linux

PROJECTS

Retail Sales & AAPL Forecasting Pipeline

● Analyzed retail sales and stock price data using Python (ARIMA/SARIMA) to generate forecasts, reducing preprocessing time by 40% and improving trend visibility

● Developed reusable analysis workflows and applied statistical validation (ADF, Ljung-Box) to ensure data accuracy and support consistent, data-driven forecasting insights

HR Attrition Prediction System

● Constructed a data analysis pipeline using Python for preprocessing and feature engineering, enabling scalable modeling workflows and achieving ROC-AUC of 0.83

● Identified high-risk employee segments through analysis and model tuning, generating insights that reduced attrition by ~5%

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