Summary
Work History
Education
Skills
Websites
Languages
Projects
Timeline
Generic
Husain Abbas

Husain Abbas

Abu Dhabi

Summary

Dedicated Data Analyst and AI Engineer with a proven ability to leverage data for improved business operations and informed decision-making. Skilled in developing machine learning models and GenAI applications, passionate about utilizing AI to drive innovation and make a meaningful impact. Committed to staying up-to-date with the latest advancements in the field of AI.

Work History

Data Excellence Engineer

Lufthansa Group


  • Worked along with the data science delivery team for reviewing various AI use cases in the Data Accelerator Program (DAP) for aviation (Baggage Handling and Crew Management).
  • Implemented Kanban boards and workflows in Jira to streamline project management, optimize resource allocation, and enhance team productivity.
  • Contributed in documenting discussion points, project plan changes, and stakeholder needs arising from review board meetings.
  • Gaining a comprehensive understanding of data quality and governance practices in the Lufthansa Group.
  • Developed positive working relationships with stakeholders of different Lufthansa Group companies to effectively coordinate work activities specifically related to data catalogue development.
  • Onboarded various data assets, explored lineage and schema functionality and took the lead in glossary development.
  • Supported in development of training videos (story and graphic design) and training manuals for Data Quality awareness, measurement and understanding.
  • Spearheaded the Data Governance Awareness Campaign to raise awareness about the topic throughout the Group.
  • Skills: Databricks, Microsoft Azure, Microsoft Purview, Python, Jira, Microsoft Power BI, Kubernetes, Scrum, Microsoft Powerpoint

Data Analyst Flexicant

Detecon International GmbH
  • Used Tableau and SQL to track KPIs surrounding marketing initiatives, and supplied recommendations to boost landing page conversion rate to 12% for a client.
  • Established KPIs and monitoring tools to assist the sales team.
  • Created monthly and quarterly reports for stakeholders using data visualization tools.
  • Created and maintained database schemas, tables, views and stored procedures.
  • Analysis of geospatial data of cities using Overpass API and gained valuable insights from the data.
  • Used Overpass Turbo (a web based data mining tool) to query Open Street Map data for data extraction.
  • Used the extracted specific data (by Overpass Turbo) in the NetWorks tool (a software system for strategic and technical planning, performance and reliability analysis and cost optimization developed by Detecon International GmbH).
  • Skills: Tableau, SQL, Microsoft Powerpoint, Overpass API, Jira

Computer Vision Werkstudent

Eagle Eye Technologies GmbH
  • Implemented an object detection and segmentation machine learning model for sign board detection.
  • Used Detectron 2 ( built by Facebook AI Research) for performing sign board detection on the dataset consisting of street images (custom dataset provided by the organization).
  • Successfully implemented three object detection algorithms - Mask R-CNN, Faster R-CNN and TensorMask.
  • Implemented an image classification model in PyTorch using neural networks to classify different vehicles on the streets (custom dataset provided by organization).
  • Skills : Python, Pytorch, Detectron 2, Tensorflow, Git and Jira.

Data Analyst

Sterlite Technologies
  • Implemented long-term pricing experiment which improved customer value by 28%.
  • Implemented A/B experiments for products to improve the conversion rate by 19 basis points and reduce the churn rate by 12 basis points.
  • Assisted in troubleshooting and resolving database errors, performance issues and data replication issues.
  • Monitored database performance and capacity, taking proactive steps to increase efficiency.
  • Generated weekly reports, detailing findings and advising recommendations for strategic decision-making.
  • Actively debug and Q/A coding in Python and SQL.
  • Participated in stakeholder meetings to present the findings after analysis.

Education

Master of Science - Information And Communication Systems

Technische Universität Chemnitz
Chemnitz, Germany
07-2025

Bachelor of Engineering - Electronics and Telecommunication

Vishwakarma Institute of Information Technology
Pune, India
06.2019

Skills

  • Data Analysis
  • Gen AI Development (LLMs): LLM fine tuning, Few shots learning, RAG, Chain of thought prompting, Multi-Agent systems, ReAct, Agents
  • Deep Learning (CNN, ANN, RNN) and Computer Vision
  • Data Governance Frameworks and AI Governance
  • Stakeholder Management
  • Project Management
  • Python, SQL, C
  • Databricks, AWS Sagemaker, Azure ML, Git
  • MS SQL Server, PostgreSQL, MySQL
  • Chroma DB, Docker, Kubernetes, Spark
  • GCP, Azure, AWS
  • Scikit-learn, Sklearn, Tslearn, OpenCV, TensorFlow, Keras, PyTorch, CUDA
  • LangChain, Hugging Face, Llama, GPT, Gemini, Claude, Anthropic, DeepSeek
  • Tableau, Power BI, Jira, Microsoft Project, Microsoft Co-pilot
  • MS Dynamics, MS Office, MS Purview, MS Sharepoint, MS Excel, MS Purview

Languages

English
Bilingual or Proficient (C2)
German
Intermediate (B1)
Hindi
Bilingual or Proficient (C2)
Arabic
Elementary (A2)

Projects

1. Classification ML model in Azure - 


  • Created a pipeline in Azure that can train, register, and deploy an ML model that can recognize the difference between tacos and burritos.
  • Set up Kubeflow in AKS cluster managed by Kubernetes, preprocessed the data, trained the model, registered it to ACR and deployed the model to AML.


2. Customer Churn Prediction with SageMaker Studio XGBoost Algorithm -


  • Using Gradient Boosted Trees to predict mobile customer departure.
  • Built a model that predicts whether a customer is likely to churn, and then how to optimally set a threshold that accounts for the cost of true positives, false positives, and false negatives.
  • Built, trained, tuned and deployed the machine learning model in AWS Sagemaker.


3. Understanding Network Usage Similarities using AI  (Master Thesis)-


  • Developed an unsupervised machine learning model, i.e a clustering model to group the different 4g network base stations according to their data usage similarities.
  • Prepared the raw data by performing steps like data cleaning, feature engineering, data parameterization and data transformation.
  • Performed time series clustering using various algorithms like Time Series KMeans, KShape and Global Alignment Kernel. Also, compared the results for choosing the most optimum algorithm.


4. Gen AI - LLM Project - Q/A Chatbot -


  • Built a system that can talk to MySQL Database.
  • User asks questions in a natural language and the system generates answers by converting those questions to an SQL query and then executing that query on MySQL database.
  • The database consists of inventory, sales aBuilt a hands-on system to fine-tune an open-source LLM on Azure ML, enabling natural-language Q&A over a custom corpus (e.g., product manuals, policy docs).nd discounts data stored in a MySQL database.
  • In the UI, store manager will ask questions in a natural language and it will produce the answers. The web app will open in your browser where we can ask questions.
  • Technologies used - Google Palm LLM, Hugging Face embeddings, Streamlit, Langchain, Chroma DB.


5. Gen AI – LLM Fine-Tuning on Azure ML – Domain-Specific FAQ Bot


  • Built a hands-on system to fine-tune an open-source LLM on Azure ML, enabling natural-language Q&A over a custom corpus (e.g., product manuals, policy docs).
  • Users submit questions via a simple web UI, and the back end serves the fine-tuned model as an API endpoint for ultra-fast, domain-aware responses.
  • Technologies used – Azure ML, MLflow, PyTorch, Hugging Face Transformers, Git, Docker, Kubernetes.


6. Gen AI – RAG System – Llama 3 Knowledge Assistant


  • Developed a Retrieval-Augmented Generation pipeline using Llama 3 and Chroma DB. The assistant ingests PDF and Markdown docs into a vector store, then uses LangChain to retrieve context and generate precise answers to user queries.
  • Perfect for building intelligent help desks or research assistants.
  • Technologies used – Llama 3, LangChain, Chroma DB, Python, Git, Docker, Streamlit.


7. Gen AI – Multi-Agent AI System – Autonomous SQL Analyst


  • Built a collaborative agent framework where distinct AI “experts” handle SQL translation, query optimization, and result analysis.
  • Uses AutoGen to orchestrate agents that convert natural-language analytics requests into SQL, execute them on Azure PostgreSQL, and summarize insights back to the user.
  • Technologies used – Azure OpenAI GPT-4, LangChain, AutoGen, Azure PostgreSQL, Docker, Kubernetes, Git.


8. Gen AI – Few-Shot Reasoning Benchmark Hub


  • Aggregated a suite of complex reasoning tasks (math, symbolic, factual QA) to benchmark LLM performance under few-shot settings.
  • Automated evaluation across datasets like MATH, BBH, HumanEval, and MMLU, providing clear visual comparisons of model capabilities.
  • Technologies used – Python, PyTorch, CUDA, Tslearn (for time-series tasks), Git.




Timeline

Data Excellence Engineer

Lufthansa Group

Data Analyst Flexicant

Detecon International GmbH

Computer Vision Werkstudent

Eagle Eye Technologies GmbH

Data Analyst

Sterlite Technologies

Master of Science - Information And Communication Systems

Technische Universität Chemnitz

Bachelor of Engineering - Electronics and Telecommunication

Vishwakarma Institute of Information Technology

Similar Profiles

CREATE PROFILE
Husain Abbas