High-level Requirements
 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
requirement.
 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.).

Focused requirements:
 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
hypothesis accordingly.
 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
Networks, etc)
- 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