Machine Learning Data Scientist

Phanindra Kumar Mulamreddy

Data Scientist (AI/ML Engineer)

+1-667-***-**** ************@*****.***

SUMMARY

With 3 years of experience in Data Engineering and AI/ML engineering, I specialize in building scalable cloud-based solutions and intelligent systems. Leveraging AI/ML frameworks like TensorFlow, PyTorch, and scikit-learn, I have developed advanced systems for medical image analysis, recommendation engines, and predictive analytics. My expertise includes designing and optimizing data pipelines for efficient ETL workflows and real-time processing, as well as implementing robust MLOps practices to ensure seamless deployment, monitoring, and lifecycle management of machine learning models on platforms such as AWS and GCP. I have achieved significant reductions in model size and latency through techniques like quantization, pruning, and A/B testing. In healthcare IT, I have integrated systems adhering to HL7 standards, ensured HIPAA compliance, and developed secure APIs to manage sensitive medical data. My experience in Agile environments includes leading cross- functional teams, driving iterative development, and delivering scalable, high-quality solutions that align with business objectives. SKILLS

● Programming/Libraries: Python (Pandas, NumPy, Scikit-learn, TensorFlow, Hugging face Jupyter), PySpark

● Databases: SQL, MongoDB, Oracle, MySql

● Machine Learning: Predictive Modeling, Supervised and Unsupervised Learning, Anomaly Detection, Feature Engineering, LLM’s

● Algorithms: KNN, Regression (Linear, Logistic, Multiple), Naive Bayes, Random Forest, SVM, NLP, K- Means

● Cloud Platforms: AWS (Glue, Redshift, SageMaker, Lambda, Athena, S3, EMR, Kinesis, Firehose, IAM)

● Big Data & Workflow Automation: Apache Airflow, Spark, Hadoop, Kafka, ETL/ELT Pipelines

● Data Engineering: Data Extraction, Data Validation, Dimensional Modeling, Data Warehousing

● Data Visualization: Tableau, Looker Studio, Power BI

● Project Management: Workflow orchestration, Agile, Software Development Life Cycle, Work Breakdown Structure, Slack, Jira

● Version Control Tools: Git, GitHub, GitLab, Bitbucket CERTIFICATES:

● AWS Machine Learning Specialty (In progress)

● AWS Data Engineer Associate

WORK EXPERIENCE:

Data Scientist (AI/ML Engineer) Broadcom March 2023 – Present

• Conducted statistical analysis and developed predictive models, reducing feature engineering workflows’ runtime by 20% and improving machine learning model training efficiency.

• Extracted and analyzed large datasets using PySpark, SQL, and AWS tools, generating actionable insights that led to a 15% increase in product performance and revenue optimization.

• Designed and implemented GPT-like large language model prototypes, achieving 10% efficiency gains over baseline transformer models and enabling scalable deployment for real-world applications.

• Applied transfer learning and fine-tuning techniques to LLMs, improving performance for domain-specific tasks like conversational AI, sentiment analysis, and document summarization.

• Conducted prompt engineering experiments to optimize LLM outputs, saving 15% in content generation costs.

• Leveraged Kafka to process over 10,000 events/second, building scalable and fault-tolerant data pipelines that reduced data latency by 25% and supported real-time analytics.

• Implemented natural language processing (NLP) pipelines for text classification, sentiment analysis, and recommendation systems.

• Stayed up to date with the latest AI/ML trends and technologies, integrating emerging techniques like deep learning, reinforcement learning, and transfer learning into projects.

• Applied statistical and machine learning techniques like regression, clustering, classification, and deep learning to drive business outcomes.

• Developed a cloud-native MLOps platform on AWS for scalable AI/ML deployment and management using Python, TensorFlow ensuring 99.9% availability while handling petabytes of medical imaging data.

• Utilized TensorFlow and PyTorch for deep learning analysis of medical images, achieving 90% accuracy and conducted A/B testing to compare model performance for data-driven decision-making.

• Employed advanced optimization techniques such as quantization and pruning (TensorFlow-Model-Optimization) to reduce ML model size by 60% without sacrificing accuracy.

• Established a scalable data ingestion pipeline on AWS (S3, Glue, Athena) for processing and storing terabytes of medical data from diverse sources, enduring reliability and scalability.

• Utilized pre-built machine learning algorithms (scikit-learn, XGBoost) for predictive analytics on medical data, resulting in an 18% improvement in diagnosis accuracy.

• Coordinated cross-functional teams to resolve critical system issues, achieving 99.9% uptime and reducing 50% incident resolution time. Programmer Analyst Cognizant, India February 2021 – June 2022

• Implemented advanced machine learning algorithms to personalize product recommendations based on user behavior and preferences, leading to a 35% reduction in response times.

• Employed advanced AI techniques like NLP and collaborative filtering to enhance product recommendations, leading to a 20% increase in customer engagement and conversion rates.

• Preparation of Understanding documents, creating knowledge repository for the application process and end to end flow docs.

• Implemented reinforcement learning techniques to optimize product recommendations over time, allowing the system to adapt and improve based on user feedback and interactions.

• Integrated Elasticsearch and Apache Kafka for real-time data processing, enabling the recommendation engine to dynamically adapt to changing user interactions and market trends.

• Perform test estimation, test planning, requirement analysis, test design, test execution, defect management and test closure activities.

• Utilized unsupervised learning algorithms such as clustering and dimensionality reduction to segment customers based on browsing and purchasing behavior, enabling more targeted and personalized recommendations.

• Conduct daily review meetings for the entire team to track and streamline the workflow.

• Leveraged machine learning models for sentiment analysis and trend forecasting to provide actionable insights to the e- commerce client, enabling data-driven decision-making and strategic planning. Operations Research Analyst TATA Capital India April 2020 – January 2021

• Utilized SQL to query and analyze large datasets, generating actionable insights that supported process optimization and strategic decision- making.

• Assisted in identifying operational inefficiencies and recommending process improvements to enhance performance.

• Assisted in developing resource allocation models by performing data analysis with SQL, contributing to a 15% improvement in project delivery timelines.

• Used tools like Excel, Python, R, and statistical software to perform quantitative analysis and modeling.

• Designed and maintained dashboards to monitor key performance indicators (KPIs) using SQL and data visualization tools, enabling real- time decision-making for operations teams.

• Operated financial market research, collected data and performed analysis by using statistical software

• Performed sensitivity analysis and risk assessment using SQL and statistical techniques to evaluate the impact of variable changes on business outcomes, enhancing decision-making accuracy under uncertainty. EDUCATION

● Master’s in Data Science from University of Maryland Baltimore County, Baltimore, MD – USA.

● Bachelor’s in Mechanical Engineering from VR Siddhartha Engineering College - INDIA.

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