Manager, Data Science

We are seeking an experienced Data Science / Machine Learning Engineering Lead to join our team and drive the development of advanced ML/AI capabilities. You will lead a team of Data Scientists / ML Engineers, focusing on building and deploying cutting-edge machine learning solutions using our modern ML infrastructure including Anthropic, OpenAI, and self-hosted LLMs.

• Team Leadership & Management

  • Lead, mentor, and develop a team Data Scientists, Data Engineers, 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

• Technical Leadership

  • Design and implement scalable ML model training pipelines using modern toolset (e.g MLflow, Comet, Langfuse, WandB, Trino, dbt, Spark, Flink, etc)

  • 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 model continous validation frameworks within our ecosystem

  • Develop real-time anomaly detection systems leveraging for 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

  • Creation of eval, 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

• Education & Experience

  • 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

• Familiarity with vector databases (Pinecone, Weaviate, Qdrant)

• Experience with model security and responsible AI practices

• Contributions to open-source ML projects