Data Scientist

Role Summary Design, develop and continuously improve AI and machine learning solutions that generate business intelligence from enterprise data. Build predictive models, ranking algorithms and AI-driven decision systems by combining data from enterprise platforms and third-party AI services. Work closely with Product, Solution Architects, AI Engineers and Data Engineers to transform business problems into production-ready AI capabilities.

Key Responsibilities

• Translate business problems into machine learning and AI solutions.
• Develop predictive, classification, recommendation and ranking models.
• Engineer features from structured, unstructured and AI-generated data.
• Design experimentation frameworks, A/B testing and model evaluation strategies.
• Develop confidence scoring, anomaly detection and business scoring models.
• Collaborate with AI Engineers and Data Engineers to productionize models.
• Monitor model performance, drift and business impact after deployment.
• Communicate analytical findings to business and engineering stakeholders.

AI & Data Science Responsibilities

• Develop proprietary business intelligence models using enterprise data.
• Work with embeddings, semantic similarity, vector search and Retrieval-Augmented Generation (RAG).
• Build attention scoring, content ranking, churn prediction, recommendation and forecasting models.
• Apply NLP, computer vision and statistical learning techniques where appropriate.
• Define feature engineering strategies and model evaluation metrics.
• Support responsible AI, explainability and bias evaluation.

Required Technical Skills

• Python (mandatory), SQL and advanced data analysis.
• PyTorch and/or TensorFlow; Scikit-learn, Pandas, NumPy.
• Statistics, probability, machine learning and deep learning.
• LLM fundamentals, embeddings, vector databases and RAG.
• Experiment tracking and model lifecycle concepts.
• Cloud AI platforms (AWS, Azure or GCP) and Git.

AI-Driven Coding & Engineering Practices (Mandatory)

• Experience using GitHub Copilot, Cursor, Windsurf, Claude Code or equivalent AI coding assistants.
• Leverage AI to accelerate feature engineering, experimentation, code generation, documentation and testing.
• Review and validate AI-generated code, notebooks and models before production deployment.
• Use AI-assisted debugging, model optimization and prompt engineering to improve productivity.
• Follow enterprise AI governance, reproducibility and version control best practices.

Preferred Experience

• Enterprise AI platforms, Media & Entertainment, OTT, AdTech, recommendation systems, personalization, customer analytics or large-scale SaaS.

Success Measures

• High-performing production models delivering measurable business value.
• Improved prediction accuracy, precision/recall or business KPIs.
• Reliable model monitoring and low model drift.
• Reusable AI assets and collaboration with engineering teams for production deployment.