Data Science Machine Learning

Anish Philip

**************@*****.*** +1-631-***-**** https://anishphilip012git.github.io/portfolio linkedin.com/in/anishphilip12 PROFESSIONAL EXPERIENCE

Chief Engineer Lead Engineer Engineer

Samsung

•Awarded "Employee of the Year" among 3,000+ employees at Samsung for delivering impactful cloud security solutions.

•Boosted user productivity by 50% by developing a 1-click SSO solution and a secure RBAC platform, improving access to 1,500+ cloud resources and streamlining IAM, using Golang, Python, Angular, and OAuth2.

•Achieved 99.9% multi-cloud availability by architecting a zero-trust framework across AWS, Azure, and GCP, utilizing FastAPI, ELK stack, Ansible, Kubernetes, and microservices.

•Optimized performance across 15+ databases (MSSQL, MySQL, PostgreSQL, Amazon Aurora) by enhancing indexing, partitioning, multi-join queries, and aggregation functions, reducing query execution time by 60%.

•Achieved 3x faster incident resolution by transitioning legacy systems to a serverless architecture, leveraging AWS Lambda, Python, and Kubernetes for cost-efficient autoscaling..

•Engineered high-performance WebSocket-powered proxy endpoint connectivity, enhancing real-time database accessibility.

•Strengthened security and compliance, reducing audit-related incidents by 50%, by implementing robust database auditing, access controls, and real-time monitoring, collaborating with the DB and security teams.

•Boosted resilience by 40% and increased scalability by 70% by streamlining firewall and package management.

•Developed a smart work allocation and AI-driven ticketing platform, reducing customer support backlog by 65%, leveraging Java, Spring, Hibernate, Golang, and TensorFlow.

•Minimized ticket resolution time to 1 day by automating task distribution using attendance data and AI-driven email classification, ensuring faster incident handling.

EDUCATION

Stony Brook University

MS Computer Science ( with specialization in Data Science )

•Machine Learning, Distributed Systems, Analysis of Algorithms, Network Security, Data Science (Skiena) GPA 3.84/4 Delhi Technological University (Formerly DCE)

B. Tech in Software Engineering

•Operating System, Database Management System, Object Oriented Programming, Computer Network GPA 9.1/10 ( Top 3% ) ACADEMIC EXPERIENCE

Graduate Research Assistant

Secure Systems Lab - Prof. R. Sekar & Prof. Scott Stoller

•Developed a full-stack privacy framework for NSF-funded research, modernizing web applications with enforceable privacy policies by leveraging Linux/UNIX security protocols, C++, React, Redux, and TypeScript.

•Designed and implemented a RBAC based no-code UX framework, streamlining data access policy enforcement.

•Built a Google Sheets-like real-time module for structured data management, integrated with CI/CD pipelines using GitHub Actions. Full Stack Software Engineer

Compas Labs - Prof. Michael Ferdman

•Digitized 80% of CS department workflows, ensuring 99.9% availability by developing a role-based system with React, Node.js, and GCP.

•Cut workflow turnaround from 3+ days to 1 day by automating document signing and reminders using Adobe PDF and Google APIs.

•Streamlined student and faculty management for 1,000+ users annually by automating enrollment and approvals with GCP App Scripts, Postal, Docker, and CI/CD for continuous deployment. Fault-tolerant Distributed Transaction System

Golang gRPC Paxos RAFT PBFT Protocol Buffers

•Achieved 99.9% durability and availability for transaction processing, with response times under 500 ms, by implementing a fault-tolerant distributed banking transaction system using gRPC and Badger.

•Engineered a scalable key-value store supporting seamless CRUD operations across 20+ replicas, utilizing a modified RAFT based consensus.

•Implemented advanced protocols (Multi-Paxos, optimized RAFT, PBFT with Optimistic Phase reduction) to ensure robust consensus in asynchronous environments with heartbeat checks, leader election, log replication and persistence, checkpointing and threshold signature. Machine Learning and Data Science (SBU)

Python Pandas PyTorch

•Privacy Policy Analysis of Medical App data

•Increased data transparency for 10,000+ health apps by breaking down complex privacy policies, empowering users to better understand data usage and privacy risks.

•Flagged 1,000+ potential privacy law violation concerns by applying TF-IDF, sentence-transformers, and Legal-BERT to analyze app permissions, consent forms, and data collection practices for regulatory compliance.

•Financial Trading System (FTS) using Reinforcement Learning :

•Optimized profits and system efficiency, achieving a 40% revenue increase by comparing state-of-the-art RL algorithms (Temporal Q- learning, LSTM, K-Line Clustering).

•Ensured consistent and accurate model evaluation, streamlining processes with zenML and MLflow. TECHNICAL SKILLS

•Languages: Golang, Python, JavaScript, TypeScript, Java, C/C++, Bash, SQL, R, HTML/CSS

•Technologies: Node.js, NextJS, Spring Boot, Angular, React, NestJS, GraphQL, REST, Kafka, OAuth, SAML, LDAP, Active Directory

•DevOps & Cloud: AWS, Azure (Certified), GCP, Kubernetes, Docker, Git, CI/CD, Jenkins, Azure AD, Hashicorp Vault, Terraform, Ansible

•Databases: MySQL, Postgres, MongoDB, DynamoDB, Amazon Redshift, Amazon RDS, Hadoop, Redis, Firebase Jul 2017 – Aug 2023 Delhi, India

Aug 2023 – May 2025 New York, USA

Aug 2013 – May 2017 Delhi, India

May 2024 – present

Jan 2024 – Dec 2024

Aug 2024 – Dec 2024

Aug 2023 – Dec 2024

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