Overview
On Site
Full Time
Skills
FOCUS
Programming Languages
Python
R
Jupyter
NLTK
scikit-learn
Unstructured Data
Named-Entity Recognition (NER)
Text Classification
Machine Learning (ML)
Management
Unsupervised Learning
Deep Learning
TensorFlow
PyTorch
Keras
BERT
Apache Velocity
Data Processing
Apache Spark
Apache Hadoop
Microsoft Azure
Analytical Skill
Problem Solving
Conflict Resolution
Workflow Optimization
Evaluation
Natural Language Processing
Statistics
Probability
Telecommunications
Customer Experience
Collaboration
Communication
Artificial Intelligence
Workflow
Job Details
Data Scientist(NLP Focus)
Programming Languages: Advanced proficiency in Python (preferred) and/or R; experience with Jupyter notebooks.
NLP Libraries & Frameworks: Strong hands-on experience with NLTK, spaCy, Gensim, Hugging Face Transformers, and Scikit-learn.
Text Preprocessing: Expertise in processing noisy, unstructured text from various data sources
Domain-Specific NLP: Familiarity with entity recognition, intent detection, and text classification
Machine Learning: Solid foundation in supervised and unsupervised learning, with applications to telecom problems (e.g., anomaly detection, predictive maintenance, customer segmentation).
Deep Learning for NLP: Experience with deep learning frameworks (TensorFlow, PyTorch, Keras) for advanced NLP tasks (LSTM, Transformers, BERT, GPT).
Data Handling: Proficient in handling large-scale, high-velocity telecom datasets; experience with distributed data processing (Spark, Hadoop) is a plus.
Evaluation: Design and interpret experiments to evaluate NLP models, including error analysis and business impact assessment.
Statistical Analysis: Strong understanding of statistics and probability as applied to telecom service quality and customer experience.
Azure experience preferred
Both positions require hands on experience with Agentic framework
Analytical & Problem-Solving Skills
Workflow Optimization: Ability to identify bottlenecks in graph-based agent flows and optimize for performance and reliability.
Data Handling: Experience with data preprocessing, postprocessing, and evaluation of AI-generated outputs.
Evaluation: Design and interpret experiments to evaluate NLP models in a telecom context, including error analysis and business impact assessment.
Statistical Analysis: Strong understanding of statistics and probability as applied to telecom service quality and customer experience.
Collaboration & Communication
Cross-Functional Collaboration: Comfortable working in teams with data scientists, product managers, and software engineers.
Stakeholder Communication: Ability to explain complex AI concepts and workflow logic to both technical and non-technical stakeholders.
Note: Job Description and Background Check
Candidates may be subjected to a Background Check /Drug Test as required by the end client before the assignment starts.
Programming Languages: Advanced proficiency in Python (preferred) and/or R; experience with Jupyter notebooks.
NLP Libraries & Frameworks: Strong hands-on experience with NLTK, spaCy, Gensim, Hugging Face Transformers, and Scikit-learn.
Text Preprocessing: Expertise in processing noisy, unstructured text from various data sources
Domain-Specific NLP: Familiarity with entity recognition, intent detection, and text classification
Machine Learning: Solid foundation in supervised and unsupervised learning, with applications to telecom problems (e.g., anomaly detection, predictive maintenance, customer segmentation).
Deep Learning for NLP: Experience with deep learning frameworks (TensorFlow, PyTorch, Keras) for advanced NLP tasks (LSTM, Transformers, BERT, GPT).
Data Handling: Proficient in handling large-scale, high-velocity telecom datasets; experience with distributed data processing (Spark, Hadoop) is a plus.
Evaluation: Design and interpret experiments to evaluate NLP models, including error analysis and business impact assessment.
Statistical Analysis: Strong understanding of statistics and probability as applied to telecom service quality and customer experience.
Azure experience preferred
Both positions require hands on experience with Agentic framework
Analytical & Problem-Solving Skills
Workflow Optimization: Ability to identify bottlenecks in graph-based agent flows and optimize for performance and reliability.
Data Handling: Experience with data preprocessing, postprocessing, and evaluation of AI-generated outputs.
Evaluation: Design and interpret experiments to evaluate NLP models in a telecom context, including error analysis and business impact assessment.
Statistical Analysis: Strong understanding of statistics and probability as applied to telecom service quality and customer experience.
Collaboration & Communication
Cross-Functional Collaboration: Comfortable working in teams with data scientists, product managers, and software engineers.
Stakeholder Communication: Ability to explain complex AI concepts and workflow logic to both technical and non-technical stakeholders.
Note: Job Description and Background Check
Candidates may be subjected to a Background Check /Drug Test as required by the end client before the assignment starts.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.