Data Science Manager

Job Description:

We are seeking an L4 Data Scientist / AI Architect Manager to lead our AI initiatives and drive the adoption of cutting-edge Machine Learning and Generative AI technologies. In this dual-hat role, you will define the technical architecture of our enterprise AI systems while directly managing and mentoring a high-performing team of data scientists and ML engineers. You will act as the crucial bridge between business strategy and technical execution, ensuring our AI solutions are innovative, scalable, and deliver measurable business value.

Responsibilities:

• Architect AI Solutions: Design and build scalable, production-ready AI/ML systems and Generative AI pipelines (e.g., RAG, fine-tuned LLMs).
• Team Leadership: Manage, mentor, and grow a technical team of data scientists and engineers, fostering a culture of excellence and continuous learning.
• Strategic Direction: Partner with Product and Engineering leadership to define the AI roadmap and translate business objectives into actionable technical deliverables.
• Technical Oversight: Lead system design reviews, conduct code reviews, and establish best practices for MLOps, model deployment, and AI governance.
• Hands-on Development: Actively contribute to complex coding tasks, rapid prototyping, and solving the hardest architectural bottlenecks.
• Cross-Functional Delivery: Collaborate seamlessly with data engineering, software development, and business stakeholders to integrate AI models into user-facing applications.

Skills:

AI/ML Architecture: End-to-end system design for scalable AI solutions. Programming: Python (Expert). GenAI & LLMs: RAG pipelines, fine-tuning, LangChain/LlamaIndex, Prompt Engineering. Deep Learning: PyTorch or TensorFlow. MLOps: Model deployment, serving, and monitoring (MLflow, Kubeflow, Docker, Kubernetes). Leadership: Technical team management, code reviews, and cross-functional stakeholder communication.

Data Processing: SQL, Apache Spark, Databricks, or Snowflake. Vector Databases: Pinecone, Milvus, Weaviate, or Qdrant. Cloud Infrastructure: AWS (SageMaker), GCP (Vertex AI), or Azure ML (plus general cloud networking/security). AI Governance: Data privacy, bias mitigation, and responsible AI frameworks. Additional Languages: C++, Go, or Java. Product Delivery: Agile/Scrum methodologies and translating business KPIs into technical metrics.