Strategic Leadership: Define the machine learning strategy aligned with business goals—ranging from customer-facing features (recommendation engines, NLP chatbots) to operational analytics.
Team Building & Management: Recruit, mentor, and lead a diverse team of ML engineers, data scientists, and MLOps specialists.
Product Lifecycle Oversight: Guide ML projects end-to-end—from ideation and prototyping through to A/B testing, monitoring, and maintenance in production.
Scale & Automation: Architect robust MLOps pipelines for model training, evaluation, deployment, and continuous monitoring (Kubeflow, MLflow, Seldon, or similar).
Cross-Functional Collaboration: Partner with Product, Engineering, Design, and Customer Success to ensure ML features are impactful, reliable, and seamlessly integrated.
Thought Leadership: Represent the company at industry events, publish whitepapers, and stay ahead of emerging trends in AI/ML.
Proven Track Record: 12+ years in ML/AI, with at least 5 years in a leadership role, managing teams of 5+.
Product-Driven Mindset: Experience taking ML features from concept to production at scale in a SaaS environment.
Technical Expertise: Strong background in supervised/unsupervised learning, deep learning frameworks (TensorFlow, PyTorch), and modern data engineering (Spark, Kafka, Redshift).
MLOps Savvy: Hands-on with containerized deployments, monitoring, feature stores, and model governance.
Communication Skills: Ability to translate complex technical concepts for executive stakeholders, customers, and non-technical teams.
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