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.
At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe... For full info follow application link.
Equal Opportunity Employer Minorities/Women/Protected Veterans/Disabled.
