Data Scientist

The Data Scientist is responsible for developing, validating, and deploying analytical and machine learning models that power the TP platform and Teleperformance’s AI-driven products. This role focuses on transforming business and operational data into actionable insights and predictive solutions — bridging analytics, machine learning, and product integration across FAB’s Foundation, Enablement, and Blueprint layers.

The Data

Scientist works closely with ML Engineers, Data Engineers, and Product Managers to deliver high-quality, explainable, and measurable AI outcomes.

Key Responsibilities Model Development & Experimentation - Design, train, and evaluate machine learning and statistical models to address key business use cases.

• Develop predictive, classification, NLP, and recommendation models to support FAB-enabled solutions.
• Conduct feature engineering, hyperparameter tuning, and model selection using modern ML frameworks.
• Data Analysis & Insights - Explore and analyze large, multi-source datasets to identify trends, correlations, and optimization opportunities.
• Perform exploratory data analysis (EDA), hypothesis testing, and A/B experiments to guide decision-making.
• Build data visualizations and reports to communicate results effectively to technical and non-technical stakeholders.
• Model Deployment & Collaboration - Partner with Data Engineers and ML Engineers to productionize models through MLOps pipelines.
• Integrate models with FAB microservices and APIs for real-time or batch inference.
• Ensure data integrity, reproducibility, and compliance with Responsible AI principles.
• Continuous Improvement & Research - Stay current with emerging AI/ML techniques, LLM capabilities, and open-source frameworks.
• Conduct POCs for new algorithms, generative AI integrations, or advanced analytics methodologies. Document models, workflows, and metrics within FAB’s AI repository Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related field. PhD is a plus.

Experience - 5+ years of experience in applied data science, analytics, or AI research.

• Proven experience building and deploying ML models in production.
• Exposure to AI-driven products, NLP, or generative AI pipelines preferred.

Technical

Skills - Strong proficiency in Python and ML libraries (Scikit-learn, TensorFlow, PyTorch, XGBoost, etc.).

• Advanced knowledge of data manipulation (Pandas, NumPy, SQL) and visualization (Matplotlib, Plotly, Power BI).
• Experience with LLMs, RAG pipelines, and prompt optimization preferred.
• Familiarity with cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex) and MLOps tools (MLflow, Kubeflow).
• Solid understanding of data pipelines, APIs, and feature stores.
• Experience with model interpretability (SHAP, LIME) and bias mitigation frameworks. Soft Skills - Analytical and curious mindset with strong problem-solving ability.
• Ability to communicate technical insights clearly and persuasively.
• Collaboration across global, multi-disciplinary teams.
• Proactive and adaptable in dynamic AI delivery environments.
• Continuous learning orientation with a focus on innovation and quality