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 product ionize 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 ,  Tensor Flow ,  PyTorch ,  XGBoost , etc.).
Advanced knowledge of  data manipulation  (Pandas, Num Py, SQL) and visualization (Matplotlib, Plotly, Power BI).

Experience with  LLMs ,  RAG pipelines , and  prompt optimization  preferred.
Familiarity with  cloud AI platforms  (Azure ML, AWS Sage Maker, 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