Data & AI Scientist

Mission
The Data Scientist designs, develops, and deploys data-driven and AI-powered solutions that create measurable business value. This role translates complex business challenges into analytical frameworks, builds scalable models (statistical, machine learning, and generative AI), and partners with cross-functional teams to drive impactful outcomes.

Key Responsibilities

Translate Business Needs into Data & AI Solutions

• Partner with business stakeholders to define objectives, KPIs, constraints, and success criteria.
• Frame problems into analytical and AI use cases (e.g., descriptive analytics, forecasting, optimization, NLP/GenAI, computer vision).
• Develop hypotheses and recommend the most effective analytical approach.
• Communicate insights, findings, and recommendations clearly to both technical and non-technical audiences.

Build High-Quality Data Foundations

• Explore, profile, and assess data quality; identify gaps and collaborate with Data Engineers on remediation.
• Design and implement feature engineering pipelines and reusable data transformations.
• Ensure proper documentation, data lineage, and governance standards are followed.

Develop and Optimize Models & Experiments

• Select appropriate algorithms, baselines, and evaluation metrics.
• Design and execute experiments (A/B testing, back testing, offline validation).
• Train, tune, and validate models while assessing robustness, bias, drift, and explainability.
• Develop and evaluate Generative AI solutions (prompt engineering, RAG architectures, fine-tuning where applicable) with appropriate safety and quality controls.
• Partner with Tech Leads and Data Engineers to productionize solutions (pipelines, CI/CD, model registry, monitoring).

Drive Business Impact & Model Lifecycle Management

• Define and track model performance KPIs (accuracy, business impact, latency, cost).
• Monitor models in production, including data quality, drift, and system performance.
• Continuously improve models through iteration and retraining strategies.
• Ensure adherence to Responsible AI standards, including documentation, traceability, privacy, and security.

Collaborate Across Cross-Functional Teams

• Work closely with Data Engineers, Data Analysts, Product Owners, Tech Leads, and business stakeholders.
• Coordinate with ICT, infrastructure, and security teams for data access, environments, deployment, and compliance.
• Contribute to both Build (data ingestion, transformation, ML/AI/GenAI development) and Run (monitoring, incident management, continuous improvement) activities.

Required Qualifications

• Bachelor's degree in Business, Information Systems, Computer Science, or a related field.

• Minimum 5 years of working experience in IT Systems

• Proven experience as a Business Analyst within ICT / ITdriven environments.

• Strong requirements gathering, documentation, and stakeholder management skills.

• Experience working with crossfunctional teams and agile or hybrid delivery models.

• Excellent communication, analytical, and problemsolving abilities.

Mission
The Data Scientist designs, develops, and deploys data-driven and AI-powered solutions that create measurable business value. This role translates complex business challenges into analytical frameworks, builds scalable models (statistical, machine learning, and generative AI), and partners with cross-functional teams to drive impactful outcomes.

Key Responsibilities

Translate Business Needs into Data & AI Solutions

• Partner with business stakeholders to define objectives, KPIs, constraints, and success criteria.
• Frame problems into analytical and AI use cases (e.g., descriptive analytics, forecasting, optimization, NLP/GenAI, computer vision).
• Develop hypotheses and recommend the most effective analytical approach.
• Communicate insights, findings, and recommendations clearly to both technical and non-technical audiences.

Build High-Quality Data Foundations

• Explore, profile, and assess data quality; identify gaps and collaborate with Data Engineers on remediation.
• Design and implement feature engineering pipelines and reusable data transformations.
• Ensure proper documentation, data lineage, and governance standards are followed.

Develop and Optimize Models & Experiments

• Select appropriate algorithms, baselines, and evaluation metrics.
• Design and execute experiments (A/B testing, back testing, offline validation).
• Train, tune, and validate models while assessing robustness, bias, drift, and explainability.
• Develop and evaluate Generative AI solutions (prompt engineering, RAG architectures, fine-tuning where applicable) with appropriate safety and quality controls.
• Partner with Tech Leads and Data Engineers to productionize solutions (pipelines, CI/CD, model registry, monitoring).

Drive Business Impact & Model Lifecycle Management

• Define and track model performance KPIs (accuracy, business impact, latency, cost).
• Monitor models in production, including data quality, drift, and system performance.
• Continuously improve models through iteration and retraining strategies.
• Ensure adherence to Responsible AI standards, including documentation, traceability, privacy, and security.

Collaborate Across Cross-Functional Teams

• Work closely with Data Engineers, Data Analysts, Product Owners, Tech Leads, and business stakeholders.
• Coordinate with ICT, infrastructure, and security teams for data access, environments, deployment, and compliance.
• Contribute to both Build (data ingestion, transformation, ML/AI/GenAI development) and Run (monitoring, incident management, continuous improvement) activities.