As an Associate QA Engineer at Numerator, I own the accuracy of consumer panel data that powers market research decisions for some of the world's largest brands. Bad data doesn't just slow down pipelines — it breaks trust. I've built the processes, validations, and root cause frameworks that stop bad data before it reaches stakeholders. On the science side, I build. Regression models. Classification pipelines. Time series forecasts. End-to-end ML workflows using Python, Pandas, and scikit-learn — with evaluation metrics that actually mean something (RMSE, AUC, accuracy) because a model no one can measure is a model no one should deploy. I'm also actively developing skills in prompt engineering and LLM interaction design — bridging structured data thinking with generative AI. What I bring to your team: → Data validation & quality frameworks (SQL, Excel, Python) → Predictive modelling: churn, classification, regression, forecasting → EDA, feature engineering, and preprocessing pipelines → KPI monitoring and market research reporting → Clear communication of data findings to non-technical stakeholders I don't just find problems in data. I build systems that prevent them.
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Results-driven AI Developer and Data Science professional with hands-on experience building and deploying ML models, automating data pipelines, and delivering predictive analytics solutions. Proficient in Python (Pandas, NumPy, Scikit-learn), SQL, and machine learning techniques including classification, regression, time-series forecasting (ARIMA), and NLP. Experienced in ETL/ELT pipeline development, data validation, anomaly detection, and KPI monitoring on large-scale datasets.
Passionate about leveraging AI, LLM/RAG concepts, and scalable ML systems to drive product innovation and customer retention.
Numerator · Full-time
Nov 2025 - Present · 6 mos
Numerator tracks purchase behaviour for 1M+ U.S. households. My job: make sure every data point in that panel is trustworthy.
Designed and owned root cause analysis workflows for panel data quality issues — identifying failure patterns, resolving discrepancies, and closing data gaps before they impacted downstream reporting.
Rebuilt data validation protocols that reduced manual QA effort significantly, cutting time-to-ready for panel datasets and enabling faster analyst turnaround.
Maintained demographic sample balance across key consumer segments — ensuring KPIs reflected accurate, representative market behaviour rather than skewed subsets.
Collaborated cross-functionally with data engineers, analysts, and business stakeholders to resolve pipeline inconsistencies and establish shared data quality standards.
SQL ,Excel, KPI ,Monitoring ,Data Validation, Root Cause, Analysis ,Panel Data
MBA in Information Technology – Bharati Vidyapeeth University
BCA in Bachelor of Computer Applications – Parul University
B.Sc. – North Maharashtra University (2019-01 – 2022-12)
