Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Manager, Data Science

Workato
City of London
1 week ago
Create job alert
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.


Our AI‑powered platform enables teams to navigate complex workflows in real‑time, driving efficiency and agility.


Why Join Us

Workato believes in a flexible, trust‑oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and look for team players who want to actively build our company.


We balance productivity with self‑care and offer dynamic work environments and a multitude of benefits.


Responsibilities

  • Lead, mentor, and develop a team of Data Scientists, Data Engineers, and ML Engineers.
  • Conduct regular 1:1s, performance reviews, and career development planning.
  • Design and implement scalable ML model training pipelines using modern toolsets (MLflow, Comet, WandB, Trino, dbt, Spark, Flink).
  • Lead fine‑tuning initiatives for commercial (Anthropic Claude, OpenAI GPT) and open‑source LLMs.
  • Architect and oversee model continuous validation frameworks and real‑time anomaly detection systems.
  • Establish ML engineering best practices for model versioning, monitoring, and deployment on Kubernetes.
  • Optimize the balance between commercial APIs and self‑hosted models.
  • Partner with product and engineering teams to identify high‑impact ML opportunities and define technical roadmap.
  • Implement robust CI/CD pipelines for ML models and monitor model performance using MLflow tracking.

Requirements / Qualifications

  • 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.
  • 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 and Flink for stream processing.
  • Knowledge of LLM fine‑tuning techniques (LoRA, QLoRA, full fine‑tuning).
  • Demonstrated ability to lead and inspire technical teams and communicate complex concepts to stakeholders.
  • Experience with agile development methodologies and cross‑functional collaboration.

Soft Skills / Personal Characteristics

  • Experience with embedding vector databases (Pinecone, Weaviate, Qdrant).
  • Experience building RAG (Retrieval Augmented Generation) systems.
  • Background in building ML platforms or infrastructure.
  • Familiarity with model security and responsible AI practices.


#J-18808-Ljbffr

Related Jobs

View all jobs

Manager, Data Science & AI - Data Science, Belfast, Derry/Londonderry

Manager, Data Science & AI - Data Science, Belfast, Derry/Londonderry

Manager, Data Science London, United Kingdom

Data Analyst/ Data Science Manager

Assistant Manager, Digital Experience and Data Science

Senior Home Actuarial & Data Science Manager

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.