Manager, Data Science

Team Leadership & Management

• Lead, mentor, and develop a team of Data Scientists, Data Engineers, and ML Engineers
• Conduct regular 1:1s, performance reviews, and career development planning
• Foster a collaborative, innovative team culture focused on continuous learning
• Coordinate work allocation and ensure timely delivery of projects
• Facilitate knowledge sharing and best practices across the team, * Design and implement scalable ML model training pipelines using tools such as MLflow, Comet, Langfuse, WandB, Trino, dbt, Spark, and Flink
• Lead fine-tuning initiatives for both commercial (Anthropic Claude, OpenAI GPT) and open-source LLMs
• Utilise self-hosted LLM infrastructure using Ray, AIBrix, and vLLM for optimal performance and cost efficiency with LoRA/QLoRA
• Architect and oversee continuous validation frameworks within our ecosystem
• Develop real-time anomaly detection systems leveraging streaming data processing
• Build predictive models for system performance, usage patterns, and automation workflow optimization
• Establish ML engineering best practices for model versioning, monitoring, and deployment on Kubernetes
• Create evaluation, validation, and metrics pipelines for models during training and inference

Strategic Initiatives

• Optimize the balance between commercial APIs (Anthropic, OpenAI) and self-hosted models for different use cases
• Partner with product and engineering teams to identify high-impact ML opportunities
• Define the team's technical roadmap aligned with company objectives
• Drive adoption of state-of-the-art ML techniques and tools
• Contribute to infrastructure decisions for scaling our ML platform

Operational Excellence

• Implement robust CI/CD pipelines for ML models in Kubernetes environments
• Monitor model performance using MLflow tracking and implement drift detection
• Manage Flink jobs for real-time feature engineering and anomaly detection
• Document processes, architectures, and decision rationale

• Master's or PhD in Computer Science, Machine Learning, Statistics, or related field
• 10+ years of hands-on experience in data science/machine learning
• 5+ years of experience leading technical teams
• Proven track record of deploying ML & LLM models to production at scale

Technical Skills

• Deep expertise in Python and ML frameworks (PyTorch, TensorFlow)
• Extensive experience with commercial LLM APIs (Anthropic Claude, OpenAI GPT-4)
• Strong proficiency with MLflow for experiment tracking and model management
• Experience with distributed computing using Apache Spark
• Proficiency with Apache Flink for stream processing and real-time ML
• Knowledge of LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning)
• Expertise in anomaly detection algorithms and time-series analysis

Leadership Skills

• Demonstrated ability to lead and inspire technical teams
• Strong communication skills to translate complex technical concepts to stakeholders
• Experience with agile development methodologies
• Track record of successful cross-functional collaboration
• Ability to balance technical excellence with business pragmatism, * Experience with AIBrix, vLLM or similar ML platform solutions
• Experience with AI code generation and anonymisation pipelines
• Knowledge of advanced prompting techniques and prompt engineering
• Experience building RAG (Retrieval Augmented Generation) systems
• Background in building ML platforms or infrastructure

• Business Insider named us an "enterprise startup to bet your career on"
• Forbes' Cloud 100 recognized us as one of the top 100 private cloud companies in the world
• Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
• Quartz ranked us the #1 best company for remote workers