As the successful candidate, you will hold degree in Data Science, Computer Science, Computer
Vision, Applied Mathematics, or a related field from a recognized and approved program. )A
Master or Ph.D. degree is preferred).
You must have at least 5 years of experience with hands-on data science, NLP, and/or machine
learning projects/products in industry.
You must also be able to bring ideas from conceptualization to productionalization (putting
models in production) using the right tools.
Having very strong expertise in data collection, cleaning, preprocessing, and wrangling is a
Expertise in handling text data from different data sources is a must.
Knowledge and experience in building knowledge graphs is necessary.
You must be fluent in either R or Python, preferably both, and familiarity with Golang is a plus.
You must be experienced in information retrieval (content recommendation, search metrics,
search query, document classification, entity recognition, topic modelling, etc.).
Tokenization, classification and preprocessing of different languages
Semantic analysis of big/continuous texts
Sentiment Analysis from paragraphs of text
Feature extraction (entities) from big block of text
Summarization and classification of topics from text
Understanding and utilization of Deep learning models for NLP
Understanding and work on intent and entity extraction from a sentence
Arabic language NER understanding and good work done in past
Duties & Responsibilities
You will be required to perform the following:
Work with stakeholders throughout the organization to identify opportunities for leveraging
company data to drive business solutions.
Mine and analyze data from company data sources to drive optimization and improvement of
product development, and business strategies.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Develop custom data models and algorithms as needed and appropriate to address problems.
Use predictive modeling to increase and optimize production facilities, revenue generation, and
other targeted outcomes.
Develop A/B testing mechanisms and test model quality and value, and validate the associated
Coordinate with different functional teams to implement models and monitor outcomes.
Develop necessary documentation as per established standards.
Sensitivity: Internal & Restricted
Machine Learning engineering / Data Science / AI Engineering
- Strong knowledge of diverse Machine Learning models and practices (Supervised, Unsupervised,
Neural Networks, etc)
- IIoT skills are bonus for Manufacturing focus
- Tools: Python, Jupyter, Keras, R, C++, OpenCV, PCA, Linux, Apache Hadoop stack, TensorFlow, Scikit-
learn, PyTorch, Caffe, Matlab, SAS, Alteryx
- Experience on implementing Machine Learning projects
- Extensive implementation knowledge of different types of ML (Supervised, Unsupervised, Neural
- Handling and inferencing with time-series data
- Responsible for working with SME, PM, Data Engineering teams to assess project feasibility
- Identify, develop, test, train, measure performance and quality of ML Models
- Monitor production models and tune their performance and quality.
- Identify resources needed and work with Data engineer to procure right ML resources
- Document ML models with problems and outcome success
- Testing ML model with the SME and confirm acceptance