Manager, Data Science & Applied AI, you will play a pivotal role in driving the implementation of impactful AI/ML solutions across the enterprise....This is a strategic and hands-on leadership position focused on developing cutting-edge ML/AI products-ranging from predictive models and recommender systems to generative AI and computer vision use cases-while ensuring robust production deployment and adoption.
1.End-to-End AI Solution Delivery & Technical Oversight
• Lead the full lifecycle of AI/ML project delivery: from problem formulation and hypothesis generation to data acquisition, feature engineering, model development, deployment, and continuous optimization.
• Design and review advanced statistical models, machine learning algorithms, and deep learning architectures for tasks such as customer segmentation, forecasting, personalization, natural language processing, image/video analysis, and generative content creation.
• Ensure solutions are built for scale, performance, and robustness using modern engineering and MLOps practices-e.g., CI/CD pipelines for models, monitoring dashboards, auto...Strategic Team Leadership & Capability Building
• Manage, mentor, and grow a high-performing team of data scientists and ML engineers by defining clear roles, growth paths, and technical competencies.
• Lead regular code reviews, model design sessions, and innovation sprints to ensure rigor, reusability, and excellence.
• Foster a culture of curiosity, continuous learning, and business alignment, encouraging contributions to internal AI knowledge bases, innovation forums, and external conferences.
• Define and implement team KPIs that measure both technical quality and business impact.
3.Business Problem Translation & Stakeholder Management
• Act as a bridge between business challenges and AI solutions by deeply understanding domain pain points and framing them into solvable data science problems.
• Partner with business stakeholders across functions (e.g., revenue, audience insights, ad sales, content strategy) to identify AI use cases with high ROI...AI Productization & Operationalization
• Ensure that models are not just proof-of-concepts but are integrated into production systems with real-time or batch inference pipelines.
• Design scalable APIs, model versioning, and deployment artifacts using best-in-class tooling (e.g., SageMaker, MLflow, Vertex AI, or Kubeflow).
• Work with DevOps and platform teams to standardize model deployment workflows, automate drift detection, and reduce time-to-market for ML products.
• Maintain a post-deployment monitoring framework to track model health, user feedback, and business performance indicators.
5....Governance, Risk Management & Ethical AI
• Partner with Legal, Compliance, and Risk functions to implement guardrails for ethical AI use, especially in sensitive areas like user profiling or content moderation.
• Establish frameworks for model documentation, reproducibility, audit trails, and change control.
• Stay abreast of global AI regulations and proactively align team practices with internal and external compliance requirements.