SPECIALIZED CHAT BOT FOR A INSURRANCE COMPANY
Anonym
(Versicherungen, 500-1000 Mitarbeiter)
Schlagwörter
Skills
Python | SQL | Git | Pyspark | R | Oozi | Airflow | Qlik Google Analytics | BigQuery | Data Studio CI/CD | AWS Sagemaker | Power BI | Tableau | Django | Selenium | PostgreSQL | flask | FastAPI | Ray | Dask | Jira | Kanban Confluence | MLFlow | Scikit-Learn | Scikit-Optimize | Optuna | HyperOpt | Hugging Face | LAMINI | H20 LLM Studio | Bagging | Boosting| Clustering | Game Theory
Neural Networks | SHAPLEY | SVM | MLIB | LSTM | Optuna | Time Series Analysis | State-Space Modeling | ARIMA | Kalman FBProphet | Gensim | SpaCy | NLTK | Stanford NLP | Transformers (Hugging Face) | LoRA| tokenization | text classification | sentiment analysis | named entity recognition |word embeddings
Neural Networks | SHAPLEY | SVM | MLIB | LSTM | Optuna | Time Series Analysis | State-Space Modeling | ARIMA | Kalman FBProphet | Gensim | SpaCy | NLTK | Stanford NLP | Transformers (Hugging Face) | LoRA| tokenization | text classification | sentiment analysis | named entity recognition |word embeddings
Projekthistorie
04/2023
-
07/2023
Fine tuning of an LLM (meta/Llama_2) to improve customer queries and Q&A. Respond more consistently, learning focus, reduce hallucination, gain knowledge on insurance data. Vector averaging of word embeddings was used to measure model performance.
Llama_2 | Hugging Face | Pytorch | Embedding Distance | LAMINI | FastAPI | Pandas | Numpy
Llama_2 | Hugging Face | Pytorch | Embedding Distance | LAMINI | FastAPI | Pandas | Numpy
01/2023
-
03/2023
MARKET RESEARCH ON LLMS
Ministry of higher education Pakistani
(Internet und Informationstechnologie, 1000-5000 Mitarbeiter)
Summary review on LLM concepts, including private and open-source models, comparison of model building architectures, various MT benchmarks (HellaSwag, HumanEval), various LLM studios (H20, LAMINI) offering such services, and ethics and trustworthiness aspects of it.
Python | H20 LLM Studio | LAMINI | Neptune | Arize | Hugging Face | Langchain | Stable LM | Fairness in NLP
Python | H20 LLM Studio | LAMINI | Neptune | Arize | Hugging Face | Langchain | Stable LM | Fairness in NLP
06/2022
-
12/2022
Research Data Scientist
MAX PLANCK INST. FOR PLASMA PHYSICS
(Wirtschaftsprüfung, Steuern und Recht, 500-1000 Mitarbeiter)
Automated Scientific Discovery – Use of ML methods to ease the cycle of scientific discovery. Implemented an automated ML platform with several explainable AI and causality frameworks.
03/2022
-
05/2022
Full Stack Data Scientist
IU International University of Applied Sciences
(Internet und Informationstechnologie, 1000-5000 Mitarbeiter)
Development of a feature-store to help boost model development process. Automation of machine learning models using the complete MLOps stack of AWS including CI/CD, model pipelining, deployment, and scheduling.
ETL scripts to load data google analytics, flatten the key/value structure of the data, and finally save it in AWS S3. Built a model for text labeling to better understand student feedbacks.
Tools use: Dask | AWS IAM | AWS MLOps | AWS CI/CD | Sagemaker Pipeline| Sagemaker Feature-store| PostgreSQL | S3 | SpaCy | NLTKr clients in materializing data-driven decisions by making use of fair and explainable machine learning models.
ETL scripts to load data google analytics, flatten the key/value structure of the data, and finally save it in AWS S3. Built a model for text labeling to better understand student feedbacks.
Tools use: Dask | AWS IAM | AWS MLOps | AWS CI/CD | Sagemaker Pipeline| Sagemaker Feature-store| PostgreSQL | S3 | SpaCy | NLTKr clients in materializing data-driven decisions by making use of fair and explainable machine learning models.
03/2017
-
12/2021
Lead Data Scientist
T-Mobile GmbH, Vienna, Austria
(Internet und Informationstechnologie, 1000-5000 Mitarbeiter)
I have more than 9 years of experience in machine learning and data science. During my employments, I have lead teams of
several data scientists, both near- and off-shore and have built use cases delivering several hundred thousand € profit, purely based on data science and ML based solutions. I have ample experience of interacting with C-level executives on progress updates and strategy road maps. I have also led a group of senior researchers from universities on several projects about model bias, privacy, and explainable AI.
several data scientists, both near- and off-shore and have built use cases delivering several hundred thousand € profit, purely based on data science and ML based solutions. I have ample experience of interacting with C-level executives on progress updates and strategy road maps. I have also led a group of senior researchers from universities on several projects about model bias, privacy, and explainable AI.
02/2016
-
07/2017
Application Engineer
Samsung SDI Graz, Austria
(Industrie und Maschinenbau, 500-1000 Mitarbeiter)
05/2012
-
02/2016
Researcher
Alpen Adria University, Klagenfurt Austria
(Internet und Informationstechnologie, 500-1000 Mitarbeiter)
Worked on my PhD and publish the PhD thesis titled "Evaluation of Time Synchronization in Real Deployments of WSN".
Did thorough modeling on time-series data and used complext analytical tools such as state-space modeling and Kalman filter.
Did thorough modeling on time-series data and used complext analytical tools such as state-space modeling and Kalman filter.
09/2009
-
12/2010
Software Engineer
Ericsson Eurolab, kohlscheid, Germany
(Telekommunikation, 1000-5000 Mitarbeiter)
02/2008
-
07/2008
Software Engineer
TRG (PVT) LTD, Lahore, Pakistan
(Internet und Informationstechnologie, 500-1000 Mitarbeiter)
03/2007
-
12/2007
Software Engineer
WATEEN Telecom LTD, Lahore, Pakistan
(Internet und Informationstechnologie, 500-1000 Mitarbeiter)
Zertifikate
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
2018
Reisebereitschaft
Weltweit verfügbar
Reisebereitschaft ist je nach Abstimmung vereinbart.
