Data Scientist

The client is looking for a Data Scientist to work in The United Arab Emirates.

As a Data Scientist in the AI Solution Evaluation & Validation team, you will serve as a key technical expert responsible for the rigorous assessment and validation of third-party AI solutions. Collaborating with Product Managers, AI Architects, and domain experts, you will perform deep technical reviews, design robust evaluation methodologies, and conduct hands-on testing to verify vendor claims, assess real-world performance, and determine solution fit for enterprise adoption. This is a highly specialized role focused not on building models from scratch, but on evaluating and validating AI technologies developed externally. Your work will ensure the organization makes informed, evidence-based investments in AI that are technically sound, trustworthy, and scalable.

Key Responsibilities:

• Technical Due Diligence & Vendor Solution Assessment:
• • Conduct deep-dive technical evaluations of third-party AI solutions including methodologies, algorithms, architecture, data dependencies, and performance claims.
  • Review vendor documentation, APIs, and specifications to assess capabilities, limitations, and integration feasibility.
  • Develop strategies to validate opaque or “black-box” models when limited transparency is available.
• Evaluation Design & Methodology Development:
• • Design and implement robust, creative evaluation frameworks tailored to different types of AI solutions (e.g., predictive models, NLP tools, computer vision systems).
  • Develop methodologies using historical, synthetic, or simulated data while ensuring data security and privacy.
  • Define appropriate evaluation metrics and statistical validation techniques, including bias and fairness testing.
• Hands-on Validation & Experimentation:
• • Execute comprehensive validation plans: data prep, API/model execution, result analysis, and comparative benchmarking.
  • Apply advanced statistical methods to assess accuracy, F1-score, ROC-AUC, precision/recall, and other key metrics.
  • Identify model biases and evaluate their impact on different population segments or operational contexts.
• Results Interpretation & Reporting:
• • Translate complex evaluation outcomes into clear, actionable insights for both technical and business stakeholders.
  • Prepare technical validation reports detailing performance, limitations, risks, and data compatibility.
  • Contribute to “Go/No-Go” decisions based on analytical findings.
• Collaboration & Knowledge Sharing:
• • Partner with Product Managers to align technical validation with strategic business goals and POC objectives.
  • Work with AI Architects and engineers to understand deployment, integration, and data flow requirements.
  • Share best practices, tools, and insights across internal teams to promote consistent evaluation standards.
  • Stay current with emerging trends in AI/ML, validation frameworks, and ethical evaluation practices.

Qualifications & Experience:

Minimum Qualifications:

• Master’s or PhD in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• 3–5+ years of experience in data science or machine learning engineering roles.
• Proficiency in Python or R, along with key libraries such as scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, etc.
• Strong understanding of machine learning techniques (classification, regression, clustering, NLP, computer vision) and their mathematical foundations.
• Hands-on experience designing and executing validation strategies, including cross-validation, A/B testing, and bias detection.
• Strong statistical analysis skills and critical interpretation of model performance.
• Proficiency in SQL and handling large datasets across different sources.

Preferred Qualifications:

• Experience working in healthcare, life sciences, or related industries, with exposure to structured and unstructured healthcare data (e.g., EHRs, claims, imaging).
• Familiarity with third-party AI evaluation or use of Machine Learning as a Service (MLaaS) platforms.
• Understanding of ethical AI principles, fairness, accountability, and transparency (FAT).
• Experience with cloud-based ML platforms like AWS SageMaker, Azure ML, or Google Cloud AI.
• Knowledge of data governance, security, and privacy (e.g., HIPAA, GDPR).
• Creative problem-solving abilities and a structured approach to working with limited or incomplete data.
• Strong written and verbal communication skills, with the ability to clearly explain complex technical findings to diverse audiences.