Manager, Data Science Lisbon, Portugal; Porto, Portugal Porto

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
- 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
- changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com.

Ultimately, Workato believes in fostering a flexible,
- 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. We also believe in balancing productivity with
- care, and we offer a vibrant and dynamic work environment along with a multitude of benefits.

Responsibilities

• Lead, mentor, and develop a team of 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 toolsets (e. g. , MLflow, Comet, Langfuse, Wand
  B, Trino, dbt, Spark, Flink, etc. )
• Lead
  - tuning initiatives for commercial (Anthropic Claude, Open
  AI GPT) and
  - source LLMs
• Utilize
  - hosted LLM infrastructure using Ray, AIBrix, and v
  LLM for optimal performance and cost efficiency with Lo
  RA/QLora
• Architect and oversee model continuous validation frameworks within our ecosystem
• Develop
  - 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
• Creation of eval, validation, and metrics pipelines for models during training and inference
• Strategic Initiatives: optimize the balance between commercial APIs (Anthropic, Open
  AI) and
  - hosted models for different use cases
• Partner with product and engineering teams to identify
  - impact ML opportunities
• Define the team's technical roadmap aligned with company objectives
• Drive adoption of
  -
  -
  - art ML techniques and tools
• Contribute to infrastructure decisions for scaling the 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
  - time feature engineering and anomaly detection
• Document processes, architectures, and decision rationale

Qualifications / Experience / Technical Skills

• Education & Experience
• Master's or Ph
  D in Computer Science, Machine Learning, Statistics, or related field
• 10+ years of
  - 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 (Py
  Torch, Tensor
  Flow)
• Extensive experience with commercial LLM APIs (Anthropic Claude, Open
  AI 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
  - time ML
• Knowledge of LLM
  - tuning techniques (Lo
  RA, QLo
  RA, full
  - 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
  - functional collaboration
• Ability to balance technical excellence with business pragmatism
• Soft Skills / Personal Characteristics
• Experience with AI platforms such as AIBrix, v
  LLM 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
  - source ML projects