About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being.

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the role

As an ML Data Engineer, you’ll sit at the intersection of analytics, machine learning, and data engineering—owning insight generation, model operations, and productised data solutions. You’ll work closely with the ML Lead to improve existing AI models in production and develop new ones, covering the full lifecycle: specifying, developing, testing, analysing, and refining.

This is a mid-level, mostly independent role suited to someone who combines strong analytical thinking with hands-on technical execution. You’ll thrive if you’re detail-balanced, process-aware, and capable of delivering results with minimal supervision in a fast-moving, research-driven environment.

• Insight & Research
• Conduct hypothesis-led analysis on large conversational datasets to uncover trends, outcome drivers, and client-ready narratives
• Build and maintain benchmark datasets powering analytics dashboards and model evaluations, ensuring clear definitions and version control
• Develop and maintain Power BI dashboards and automated reporting pipelines that deliver actionable insights to internal and external stakeholders
• Translate raw conversational and model data into clear, evidence-based recommendations
• ML Ops Lifecycle (Operate & Improve)
• Own ML performance monitoring — managing experiment tracking, model registries, and performance dashboards
• Track model drift, bias, and degradation; design and execute improvement experiments
• Manage training data pipelines, feature store freshness, and ensure full data lineage and reproducibility
• Partner with engineers to define automated evaluation frameworks, including bias detection, acceptance thresholds, and rollback procedures
• Product & Data Engineering
• Collaborate with product teams to integrate analytical outputs and dashboards directly into the platform’s portal and APIs
• Work closely with data engineering teams to design scalable ingestion, storage, and transformation pipelines across PostgreSQL, NoSQL, and data lake architectures
• Support internal Copilot and automation features that rely on trusted, version-controlled metrics and insights
• Client Analysis & Delivery
• Run client-specific analytics projects, from data exploration to narrative presentation
• Communicate findings clearly to both technical and non-technical stakeholders
• Ensure analytical outputs align with business objectives and client strategy
  

• 3–4 years of professional experience in data analysis, ML analytics, or MLOps
• Proficiency in Python (pandas, NumPy, scikit-learn) and SQL for data wrangling and analysis
• Experience with Power BI, Power Query (M), and DAX for data visualization and reporting
• Familiarity with data warehousing, ETL, and statistical testing (A/B testing, bias checks)
• Experience with cloud data services (AWS or Azure) and MLOps tooling (feature store, experiment tracker, model registry)
• Working understanding of LLM concepts, evaluation methods, and prompt engineering
• Excellent communication skills and a structured, analytical approach to problem-solving
  

• Exposure to healthcare or life sciences datasets
• BI/Analytics certifications (e.g., Microsoft Data Analyst, AWS Data Analytics)
• Hands-on experience with LLMs or generative AI frameworks (OpenAI, Hugging Face, LangChain)
• Understanding of conversational AI data, including prompt optimization and model fine-tuning techniques
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field. Postgraduate education or certifications in ML/AI are advantageous
  

At Smart Working, you’ll never be just another remote hire.

Be a Smart Worker — valued, empowered, and part of a culture that celebrates integrity, excellence, and ambition.

If that sounds like your kind of place, we’d love to hear your story.