Description

Data & AI Consultant with 5+ years of experience helping organizations leverage AI, machine learning, analytics, and Generative AI to solve business challenges and drive measurable outcomes. Experienced in designing and implementing AI-powered solutions, including predictive analytics, NLP, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, process automation, and business intelligence solutions.

My background includes delivering enterprise-scale projects across healthcare and technology environments, working closely with stakeholders to identify opportunities, define AI roadmaps, and translate complex data into actionable business insights. Skilled in Python, SQL, Power BI, cloud platforms, machine learning frameworks, and modern AI technologies including OpenAI, Gemini, and Llama.

I have hands-on experience building RAG-based applications, developing LLM-powered workflows, and exploring AI Agent architectures using frameworks such as LangChain and related orchestration tools. I help organizations move from AI experimentation to production-ready solutions that improve efficiency, decision-making, and business performance.

Core Areas: Generative AI • LLM Applications • RAG Systems • AI Agents • Machine Learning • Predictive Analytics • NLP • Data Strategy • Business Intelligence • Automation

Languages

English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Medgo2u,
Data Scientist
TECH
April 2024 - Today (2 years and 2 months)
United Kingdom
● Collaborated with cross-functional teams and non-technical stakeholders to define project objectives, gather requirements, and deliver data-driven solutions that support business goals and improve operational efficiency
● Developed a machine learning–based error detection system for clinical notes using the BioClinical BERT model, enabling near real-time identification of contextual inconsistencies and significantly improving documentation quality
● Built a text summarization model to convert ASR transcripts into structured clinical chart notes using the BART large language model (LLM), improving the efficiency and readability of clinical documentation
● Designed and implemented an AI-based document processing system using OCR and NLP techniques to automatically extract and classify key information from clinical documents, including patient names, provider details, dates of birth, and service dates
● Applied Natural Language Processing techniques such as TF-IDF feature extraction, Named Entity Recognition (NER), and multiclass classification models to improve information extraction accuracy from healthcare records
● Built automated data preprocessing and feature engineering pipelines to clean, transform, and structure large clinical datasets for machine learning model training and analytics
● Designed data pipelines and optimized SQL queries to process and analyze large datasets, reducing data processing time by 50% and enabling faster generation of analytical insights
● Developed interactive dashboards and reports to communicate model insights, performance metrics, and key findings to stakeholders and decision-makers
● Built an NLP-based clinical document processing system that improved information extraction accuracy by 35% and reduced manual review time by 40%
● Optimized SQL queries and data transformation processes, reducing data processing time from 8 hours to 4 hours and improving data availability for analytics teams
Python Data science Strategic planning Marketing Business development
Wipro,
Data Specialist
November 2021 - August 2024 (2 years and 9 months)
India
• ● Designed and implemented feature engineering techniques to improve model performance and increase predictive accuracy across multiple datasets
• ● Conducted A/B testing and statistical hypothesis testing to evaluate the effectiveness of product improvements and business strategies
• ● Implemented data preprocessing techniques, including missing value handling, outlier detection, normalization, and categorical encoding, to prepare datasets for machine learning models
• ● Applied time series forecasting techniques to analyze historical trends and predict future demand patterns
• ● Collaborated with data engineers and analysts to integrate machine learning outputs into reporting workflows and business dashboards
• ● Built automated model evaluation pipelines to monitor model performance and ensure reliability over time
• ● Created visualizations using Python libraries such as Matplotlib and Seaborn to communicate complex analytical insights to stakeholders
• ● Designed data-driven solutions for customer segmentation and behavioral analysis using clustering algorithms such as K-Means and hierarchical clustering
• ● Delivered data storytelling presentations that translated technical findings into actionable insights for business leaders and non-technical teams
Data analysis Machine learning Tableau software Business plan Strategic planning