Job at a Glance
- 📍 Location: Sharjah, Sharjah Emirate, United Arab Emirates
- 🏢 Company: Crescent Petroleum
- 💼 Employment Type: Full-time
- 📊 Seniority: Not Applicable
- 🗂️ Function: Engineering and Information Technology
- 🏭 Industry: Oil and Gas
- 📅 Date Posted: 2026-08-14
- 🌐 Category: Artificial Intelligence
About the Role & Company
The Senior Data Scientist vacancy at Crescent Petroleum sits in Sharjah, Sharjah Emirate, United Arab Emirates. The Artificial Intelligence group at Crescent Petroleum has been on an upward hiring curve — a good signal for anyone joining now. Whether you’re mid-career or a step above, this kind of role tends to pay back in exposure and lateral growth. Core Senior Data Scientist deliverables sit at the center, with cross-functional work layered on when priorities shift. Regionally, Dubai leads on both pay and visibility for artificial intelligence work; that hasn’t changed this year. Mid-career candidates can deepen technical range while picking up regional exposure. Early-career applicants get live problems from day one — which beats training-heavy graduate schemes for anyone who learns by doing.
Key Requirements
- Working knowledge of this position requires strong domain hands-on experience in Oil & Gas or Manufacturing industries, with a deep understanding of operational processes, asset performance, and industrial data ecosystems
- Use domain hands-on experience in Oil & Gas or Manufacturing to design context
- Demonstrated minimum 10 years of hands
- on experience in the design, build, deploy and operate Machine Learning algorithms for industrial use cases such as reliability monitoring and predictive maintenance
- Mandatory experience in Oil & Gas or Manufacturing industry environments
- Demonstrated domain hands-on experience in industrial operations, asset management, process optimization, or predictive maintenance
- Proven experience designing and deploying ML solutions in real
- Experience with scalable ML systems (e
Job Description
The Senior Data Scientist is expected to handle designing, developing, and deploying advanced Machine Learning (ML) solutions for industrial applications such as reliability solutions, predictive maintenance, and operational/production optimization. The role focuses on building scalable, production-grade ML models that deliver measurable business value within complex Oil and Gas environments.
This position requires strong domain hands-on experience in Oil & Gas or Manufacturing industries, with a deep understanding of operational processes, asset performance, and industrial data ecosystems. The the right person will work closely closely with engineers, subject matter experts, and business stakeholders to explore, validate, interpret, and operationalize data-driven solutions. The role demands both independent project leadership and effective cross-functional teamwork.
Design, build, and deploy Machine Learning algorithms for industrial use cases such as reliability monitoring and predictive maintenance.
Build supervised and unsupervised learning models including regression and classification techniques
Apply strong mathematical principles (linear algebra, calculus, probability, statistics) to model development and optimization.
Build scalable ML solutions using distributed computing frameworks (e.g., MapReduce, streaming technologies).
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this Senior Data Scientist opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, Machine Learning, MLOps, Statistics, TensorFlow, PyTorch, Deep Learning, NLP, LLM, Computer Vision. ATS parsers match exact strings, not synonyms.
- Use a single-column layout. Two-column resumes break parsers — text gets scrambled or dropped.
- Save as .docx or standard .pdf (not scanned/image PDF). Avoid text boxes, headers, footers, graphics.
- Use standard section headings: Experience, Education, Skills, Certifications.
- Mirror the exact job title (Senior Data Scientist) in your target role line where truthful.
- Quantify results with numbers, percentages, currency. Metrics improve ATS scoring and human review.
- Spell acronyms out once (e.g., Search Engine Optimization (SEO)) so parser catches both forms.
- Font: Arial, Calibri, or Times New Roman at 10-12pt. Fancy fonts can render as unreadable glyphs.
Top 10 Interview Questions for This Role
- Walk me through your background and why you’re interested in this role.
- Describe a challenging project you owned end-to-end. What was the outcome?
- Tell me about a time you disagreed with a stakeholder. How did you handle it?
- How do you prioritize when everything on your plate feels urgent?
- How do you decide between fine-tuning a model vs. prompt engineering?
- Explain overfitting to a non-technical stakeholder.
- Walk through your data validation pipeline for training data.
- How do you evaluate model performance beyond accuracy?
- Describe your MLOps setup — from experimentation to production.
- How do you handle model drift once a system is in production?
How to Apply
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