Applied Scientist - AI/ML

GSS Group; Dubai; On-site; AED 240,000 - 420,000; Full time

Job summary

GSS Group is seeking Applied Scientists to design, develop, experiment with, and evaluate advanced AI/ML solutions that address real-world business problems in Dubai. Roles involve framing ML problems, building models, and deploying them with MLOps support.

Collaboration across data, software, and product teams is essential to deliver reliable AI outcomes. We welcome candidates from junior to senior levels, capable of leading experiments, mentoring others, and driving responsible AI practices

Qualifications

• MSc or PhD in Computer Science, ML, AI, statistics or related field.
• Strong Python and ML framework knowledge.
• Experience with ML experimentation and production deployment.
• Experience collaborating with data engineers and ML platforms.

Responsibilities

• Translate business problems into well-defined ML problems with clear objectives.
• Design, develop, train and evaluate ML/AI models based on requirements.
• Collaborate with Data Engineers, Software Engineers and MLOps to productionise models.
• Define experimentation strategies and establish baselines.
• Perform statistical analyses, experimentation, error analysis and model validation.
• Contribute to model monitoring, lifecycle management and continuous improvement.
• Communicate findings to technical and non-technical stakeholders.

Skills

Python proficiency

ML & AI fundamentals

Experimentation

Communication

Leadership (senior)

Education

MSc/PhD in CS/ML/AI/Statistics

Tools

PyTorch

TensorFlow

scikit-learn

Job description

We are looking forApplied Scientistsacross different experience levels to design, develop, experiment with, and evaluate advancedAI/ML solutionsthat solve real-world business problems.

The role involves taking machine learning problems fromproblem framing and experimentation through model development, evaluation, and production deployment. Depending on experience, the successful candidate may also contribute to scientific leadership, mentor other team members, and help establish best practices across applied research and machine learning delivery.

We are looking for candidates with strong foundations in machine learning who can balancestate-of-the-art techniques with practical business requirements, delivering reliable and measurable AI/ML solutions.

Key Responsibilities

• Translate business problems into well-definedmachine learning problemswith clear objectives and measurable success criteria.
• Design, develop, train, and evaluatemachine learning and AI modelsbased on business requirements.
• Apply appropriate techniques across areas including:
  • Classical Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Generative AI
  • Large Language Models (LLMs)
• Define experimentation strategies, establish appropriate baselines, and conduct rigorous model evaluation.
• Performstatistical analysis, experimentation, error analysis, and model validationto assess model performance.
• Work closely with Data Engineers to define data requirements, feature pipelines, and dataset quality standards.
• Collaborate with Software Engineering and MLOps teams toproductionise machine learning models and AI solutions.
• Contribute to model monitoring, performance tracking, model lifecycle management, and continuous improvement.
• Apply responsible AI principles, consideringfairness, explainability, robustness, privacy, and reliability.
• Communicate technical findings, experimental results, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.
• Contribute to technical documentation, research, experimentation, and knowledge-sharing activities.
• For experienced candidates, provide scientific leadership, review ML work, mentor junior scientists, and help raise the overall technical standard of the team.

Required Technical Skills

Machine Learning & AI

Strong understanding of machine learning and deep learning concepts, including experience with relevant techniques across:

• Supervised and unsupervised learning
• Classification and regression
• Model evaluation and validation
• Feature engineeringDeep learning
• NLP
• Computer Vision
• Generative AI / LLM-based applications

The specific depth expected will vary based on the candidate's experience level.

ML Frameworks

Experience with one or more of the following:

• PyTorch
• TensorFlow
• scikit-learn

Strong candidates should demonstrate the ability to select appropriate frameworks and modelling approaches based on the problem being solved.

Programming

• Strong proficiency inPython.
• Experience developing machine learning experimentation and modelling workflows.
• Ability to write clean, maintainable, and reproducible code.

Experimentation & Model Evaluation

• Strong understanding of experimental design.
• Statistical analysis and hypothesis-driven experimentation.
• Model evaluation and benchmarking.
• Baseline development.
• Error analysis.
• Model validation and performance optimization.

Production ML & MLOps

Experience with taking ML models or AI solutions from experimentation into production, including:

• Model deployment
• Model monitoring
• Model lifecycle management
• MLOps workflows
• Collaboration with engineering and data teams
• Cloud-based machine learning environments

Experience with cloud ML platforms and MLOps tooling is highly desirable.

Generative AI / LLM Experience

Experience withGenerative AI and LLM-based solutionswill be highly valued, particularly experience taking such solutions beyond experimentation into production.

Relevant experience may include:

• LLM-based applications
• Generative AI solutions
• Model evaluation
• Prompt-based experimentation
• AI application developmentProduction deployment and monitoring of GenAI solutions

Responsible AI

Candidates should understand the importance of responsible AI and, where relevant, demonstrate experience considering:

• Fairness
• Explainability
• Robustness
• PrivacyModel reliability
• Responsible model deployment

Collaboration & Stakeholder Management

• Work closely withData Engineers, Software Engineers, MLOps teams, Product teams, and business stakeholders.
• Clearly communicate technical findings and modelling trade-offs.
• Translate complex scientific concepts into understandable recommendations for non-technical stakeholders.
• Collaborate effectively in cross-functional and Agile environments.

Leadership & Mentoring

For experienced candidates:

• Provide scientific leadership within the squad.
• Review modelling approaches and scientific work.
• Mentor Applied Scientists and junior ML practitioners.
• Establish and promote strong experimentation and modelling practices.
• Contribute to the overall applied research and machine learning standards of the team.

For junior candidates, prior mentoring or leadership experience is not mandatory.

Qualifications

• MSc or PhD in:
  • Computer Science
  • Machine Learning
  • Artificial Intelligence
  • Statistics
  • Mathematics
  • Data Science
  • or another relevant quantitative discipline
• Equivalent practical industry experience may also be considered.

Experience Levels

We welcome candidates across0–15+ years of experience.

Junior / Entry-Level

Suitable candidates may have:

• Strong academic foundation in ML/AI.
• Relevant MSc/PhD or equivalent project experience.
• Strong Python and ML framework knowledge.
• Research, thesis, internship, or practical ML project experience.

Mid-Level

Candidates should demonstrate:

• Independent ML model development.
• Strong experimentation and evaluation experience.
• Experience working with data and engineering teams.
• Exposure to production ML or MLOps environments.

Senior / Lead-Level

Candidates should additionally demonstrate:

• End-to-end ownership of production ML solutions.
• Strong scientific and technical leadership.
• Experience with GenAI/LLM applications where relevant.
• Mentoring and scientific review capabilities.
• Strong stakeholder communication and decision-making skills.