Manager, Data Science

About Workato
Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today's fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.

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. If this sounds right up your alley, please submit an application. We look forward to getting to know you!

Also, feel free to check out why:

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• 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
  

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.

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• Team Leadership & Management
  
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  • 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
  
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  • 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
  
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  • 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
  
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  • 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
    
  
  
  

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• Education & Experience
  
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  • 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
  
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  • 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
  
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  • 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
  
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  • 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
    
  
  
  

(REQ ID: 2252)