Muskaan Mahindrakar
Data Engineer/Scientist with ML/AI proficiency | NLP, Gen AI, MLOPS | Python, SQL | Cloud, Big Data, Data Analytics
- Role
- Data Scientist at AT&T
- Location
- New York, NY, US
- LinkedIn followers
- 500 followers
About Muskaan Mahindrakar
Spent the last 6 years living at the intersection of data engineering and data science -…
Experience
Data Scientist
Feb 2024 — Present · US
Designed and optimized ETL pipelines on Google Cloud (BigQuery, Regex Dataproc, Vertex AI), Apache Spark, Hadoop, and Snowflake, improving query speed by 25% and reducing storage cost by 20% using NumPy and pandas.Engineered high-throughput data pipelines with Kafka, Spark, and Snowflake, processing multi-terabyte financial datasets, reducing latency by 40% and tripling scalability (3×).Architected fraud analytics platforms on AWS (Redshift, S3, Glue, Kinesis, Firehose) and Azure/GCP, implementing IAM-based access control to ensure security and compliance across hybrid cloud environments.Automated infrastructure provisioning using Terraform and CloudFormation, decreasing deployment time by 40% and ensuring 24/7 high-availability of fraud data pipelines.Designed and deployed RESTful APIs and microservices with Flask and FastAPI to deliver real-time ML predictions; integrated MongoDB and DynamoDB, cutting data retrieval latency by 35% and sustaining 1M+ monthly API requests at 99.9% uptime.Engineered real-time transaction monitoring pipelines, achieving SOX/GDPR compliance and boosting fraud detection accuracy by 40%.Built interactive dashboards in Tableau and AWS QuickSight, enhancing KPI visibility by 30%.Implemented CI/CD pipelines for data workflows on GCP and AWS using GitHub Actions, Airflow, and Snowflake Tasks, increasing deployment efficiency by 20% with zero downtime.Integrated anomaly detection modules within ETL workflows, improving fraud detection precision by 15% and reducing false positives in production systems.Partnered with data science, product, and DevOps teams to optimize financial data pipelines and authored comprehensive ETL documentation, improving operational reliability by 25%.
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