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:

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

• 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