Manager, Data Science

**About Workato**: **Why join us?**: Ultimately, Workato believes in fostering a **flexible, trust-oriented culture that empowers everyone to take full ownership of their roles**. We are driven by **innovation**and looking for** team players**who want to actively build our company. But, we also believe in **balancing productivity with self-care**. That's why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives. Also, feel free to check out why: - 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 **Responsibilities**: 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 **Requirements**: **Qualifications / Experience / Technical Skills**: - **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 **Soft Skills / Personal Characteristics**: - 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, Weavi