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Laura Halacheva

Data Science Developer

Laura Halacheva

Data science lead for Teva Pharmaceuticals.

Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.

Data Science
Data Modeling
Data Statistics
Machine Learning
Artificial Intelligence
Data Analysis
Data Visualization
Text Analysis
Natural Language Processing
Genetic Algorithms
Linear Models
Semantic Text Analysis
Laura Halacheva

Laura Halacheva

Data science lead for Teva Pharmaceuticals.

Data Science Developer

Laura is Available for Projects

Work with Laura

Employment Highlights

Data Science Specialist

Independent

June 2018 - Present (5 years 10 months)

Lead Data Scientist, Text Analysis

Ontotext

April 2012 - May 2018 (6 years 2 months)

Education Highlights

BSc, Computer Science

University of Bucharest

September 1999 - June 2003 (3 years 10 months)

Portfolio

www.canada.ca

Canadian Heritage Information Network

www.canada.ca

Canadian Heritage Information Network

Resume

Data Science Specialist

Independent

June 2018 - Present (5 years 10 months)

  • Led projects in various industries, including conversational AI, document analysis, retail, and credit risk assessment. Also experienced with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. 
  • Clients: MobiBiz, Canadian Heritage Information Network (CHIN), and more.

Lead Data Scientist, Text Analysis

Ontotext

April 2012 - May 2018 (6 years 2 months)

Ontotext is a technology company specializes in semantic platforms that identify meaning across unstructured data.

  • Worked as the lead data scientist of the R&D department.
  • Developed machine learning models in Edlin, an in-house library for NLP written in Java; methods for domain adaptation; methods for automated feature selection; methods for optimization of F-measure. Models were linear or SVM, for classification and sequence classification.
  • Built a machine learning model for classification of tweets as either Rumor/Not Rumor. Model implemented in R, integrated in a Kafka pipeline supported by Ontotext.
  • Worked with DBpedia as an RDF database, such as for the food and drink project; very familiar with its structure, including pages, categories, subcategories, lists, topics, parallel languages, and respective coverage.
  • Experienced with automated and semi-automated integration of various RDF resources, such as DBpedia and Geonames.

Education

BSc, Computer Science

University of Bucharest

September 1999 - June 2003 (3 years 10 months)