Data Scientist / ML Engineer

Location: Seoul, Republic of Korea (hybrid and remote flexible)

About Enolink

Enolink builds Enobase, a secure platform for healthcare data collaboration, enabling institutions and research organizations to access, analyze, and collaborate on sensitive data while preserving privacy. We work at the intersection of data infrastructure and the healthcare and life sciences sector to help our partners turn data into better outcomes.

About the Role

We are looking for a Data Scientist / ML Engineer to join a client engagement involving production and quality data in a regulated industry setting. This role goes beyond analysis. You will build machine learning models and AI tools, and you will be responsible for getting them into reliable production use, not just prototyping them.

This engagement is expected to grow significantly in scope over time. It begins with a focused initial phase and, based on results, has strong potential to expand into a much larger, organization wide engagement with the same client. We are looking for someone who wants to grow into that larger scope, including eventually owning the MLOps practices that let models run reliably at scale.

We also expect this person to work as an AI augmented engineer. At Enolink, AI coding and analysis assistants are part of the standard toolkit rather than a shortcut. We want someone who uses them to move faster while staying fully accountable for what ships: someone who directs the tools, reviews every output, and knows when to write the code themselves.

What You Will Do

• Design and build data ingestion and preprocessing pipelines for structured production and manufacturing datasets

• Perform exploratory data analysis to identify patterns, sources of variability, and quality signals

• Build, train, and iterate on machine learning models and AI tools suited to the client’s operational questions

• Take models from prototype to production, including versioning, testing, deployment, and monitoring for drift and performance degradation

• Use AI coding assistants to accelerate delivery while owning the architecture decisions, code review, and verification that keep the output production ready

• Establish MLOps practices such as CI/CD for models, retraining pipelines, and reproducibility as the engagement matures

• Collaborate closely with our platform engineering team to deploy and run your work within Enobase, our data platform built on Kubernetes

• Communicate findings and model behavior clearly to both technical and business stakeholders

• Document your data workflows, models, and MLOps setup for reproducibility and handoff

• Share the prompting patterns, review habits, and tooling that work in practice, so the wider team compounds the same gains

• As the engagement scales, help extend successful models and infrastructure across a broader set of data, use cases, and stakeholders within the client’s organization

What We Are Looking For

• Strong foundation in data science, machine learning, or applied AI (advanced degree preferred but not required)

• Practical experience building and deploying ML models in production, not only training them in notebooks

• Working MLOps experience covering model versioning and registry, CI/CD for ML, containerized deployment, and monitoring (for example MLflow, Kubeflow, or similar)

• Strong Python skills and standard ML and data tooling (pandas, scikit-learn, PyTorch or TensorFlow as relevant)

• Experience with structured and tabular data from real world operational or production systems (time series, sensor, or process data is a plus)

• Comfortable working independently early on, with the ability to operate at larger scale and help shape a growing team as the engagement expands

• Strong written and verbal communication skills, able to explain model behavior and trade offs to a business audience

Working With AI Tools

This is a core requirement for the role rather than a bonus. We measure output by what reaches production and holds up there, and we expect AI tools to be used deliberately to raise that bar.

• Fluent daily use of AI coding and analysis assistants (for example Claude Code, Cursor, Copilot, or equivalent) across the full workflow: exploration, pipeline development, model code, tests, and documentation

• Ability to break a problem into clear specifications and scoped tasks that an assistant can execute well, rather than issuing vague prompts and accepting whatever comes back

• Disciplined review of generated code and analysis. You read it, test it, and can explain every line you ship

• Sound judgment on where AI accelerates work and where it does not, including complex system design, subtle data quality issues, and anything touching regulatory or client specific constraints

• Strong testing and validation instincts, since faster generation only creates value once correctness is verified

• Care with data handling and confidentiality when using AI tools, particularly with regulated client data

• Interest in improving how the team works with these tools over time, including internal tooling, reusable prompts, and review standards

To be clear on what we do not mean: we are not looking for someone who hands their thinking to a model. We are looking for an engineer whose judgment stays in control while the tools carry the volume.

Nice to Have

• Experience in regulated industries such as healthcare, biotech, pharmaceuticals, or manufacturing quality control

• Background in anomaly detection, process and quality analytics, or continual learning

• Familiarity with Kubernetes and cloud infrastructure on AWS, and comfort working alongside platform and DevOps engineers

• Experience building internal tooling or automation with AI assistants

• Korean language proficiency

Engagement

• Enolink is flexible on structure for the right candidate. This can be discussed during the interview process based on your situation and availability. This role has a clear path to grow in scope and commitment as the engagement expands.