Aakanksh Chittiprolu

About Aakanksh Chittiprolu

Multidisciplinary Data Scientist and Data Engineer with 4+ years of proven experience building scalable ML systems, data pipelines, and real-time analytics platforms across healthcare, retail, and consulting domains. Expert in designing full-stack ML solutions from raw data ingestion and transformation to model development, deployment, and MLOps workflows delivering end-to-end business impact in production environments. • Machine Learning & AI: Developed and deployed models for classification, regression, time-series forecasting, NLP (BERT, spaCy), and recommender systems, achieving up to 22% improvement in forecast accuracy, 17% churn reduction, and 28% boost in product upselling. • Data Engineering & Real-Time Processing: Designed enterprise-grade ETL pipelines using Spark, Airflow, and Kafka; migrated batch to streaming architecture, reducing data latency from 24 hours to under 10 minutes. • Cloud & Platforms: Hands-on with AWS (Glue, S3, Redshift, Lambda), Azure (ADF, Synapse, Databricks), and Snowflake to build cost-efficient, secure, and scalable cloud-native data platforms. • MLOps & DevOps: Implemented MLflow, Docker, and CI/CD pipelines (GitHub Actions) for model versioning, containerized deployment, and continuous integration of production-ready ML services. • Visualization & Stakeholder Communication: Delivered executive dashboards and real-time analytics using Power BI, Tableau, and Plotly to translate complex data into actionable business insights.

Experience

1. Data Scientist

   Brainvire

   Apr 2025 — Present · US

   Built classification models (XGBoost, Random Forest) to automate support ticket triaging, reducing firstresponse SLA breaches by 37% and improving CSAT.• Developed SKU-level demand forecasting using Prophet and LSTM models; improved forecast MAE by22% and optimized inventory planning.• Designed churn prediction pipelines with logistic regression and ensemble stacking; enabled earlyintervention strategies, lowering churn by 17%.• Delivered scalable NLP systems (BERT, spaCy) for sentiment mining and topic modeling across 100K+reviews; insights drove product feature roadmap.• Engineered hybrid recommender systems (collaborative filtering, matrix factorization) resulting in 28%increase in cross-sell and upsell conversions.• Automated Azure-based ETL workflows using Data Factory + Spark notebooks, cutting manual prep by65% and enabling daily batch pipelines.• Operationalized ML pipelines with MLflow + Azure ML for tracking, model registry, A/B deployment, andreproducibility compliance.• Designed Power BI dashboards embedded with real-time model KPIs and service metrics, drivingexecutive-level visibility and accountability.• Led Agile ceremonies, collaborated with PMs, DevOps, and analysts to ensure continuous delivery andstakeholder feedback integration.

Education

• Christ University, Bangalore

  Bachelor of Technology - BTech

  2017 — 2021

• University of Delaware

  Master's degree

  2023 — 2025