Senior Data Scientist

Data Scientist

Overview

A leading company in its’ fill in Hong Kong is looking for a hands-on, end-to-end Data Scientist to design, build and deploy machine learning solutions across business-critical use cases. This is a high-impact role for someone who enjoys owning the full lifecycle, from data extraction and transformation through model development, production deployment, monitoring and continuous optimisation.

The role sits at the intersection of data, engineering and business, and will suit someone who is equally comfortable writing production-grade code, building models, and partnering with stakeholders to turn complex problems into scalable solutions.

Responsibilities

• Own the end-to-end lifecycle of machine learning and advanced analytics solutions, from data sourcing and preparation through deployment and post-production enhancement.

• Work with structured and unstructured data from enterprise platforms, APIs and operational systems.

• Build predictive, classification, recommendation, optimisation and NLP/LLM-enabled solutions depending on the business need.

• Develop robust, reusable code and scalable data pipelines using Python and SQL. • Partner with engineering teams on data ingestion, transformation and feature pipeline

design. • Deploy models through APIs, batch workflows or real-time services. • Apply modern MLOps and DevOps practices including version control, automated

testing, CI/CD, containerisation, model versioning and performance monitoring. • Build dashboards or lightweight user-facing tools where needed to improve adoption

of data products. • Ensure strong standards around data quality, explainability, governance,

documentation and model performance. • Translate commercial or operational challenges into measurable analytical solutions

and communicate insights clearly to both technical and non-technical stakeholders.

Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering or a related discipline.

• Around 4–6 years of relevant experience in data science, machine learning, advanced analytics, data engineering or a related technical field.

• Strong hands-on coding skills in Python and SQL; PySpark experience would be highly valued.

• Proven track record of building and deploying models into production, rather than working only on proof-of-concepts.

• Strong understanding of data extraction, feature engineering, ETL/ELT, data modelling and data-quality management.

• Experience with machine learning libraries such as scikit-learn, XGBoost, LightGBM, TensorFlow or PyTorch.

• Exposure to cloud platforms such as Azure, AWS or GCP, and modern data environments such as Databricks, Snowflake or similar.

• Familiarity with software engineering and DevOps tools including Git, CI/CD, Docker, Kubernetes and API development frameworks such as FastAPI or Flask.

• Experience with MLOps, experiment tracking and model monitoring is strongly preferred.

• Prior exposure to insurance, financial services, fintech, consulting or other regulated environments would be advantageous.

• Strong ownership mindset, commercial awareness and ability to work across both technical and business teams.

• Good command of English; Cantonese or Mandarin would be a plus.

Why This Role

This is an excellent opportunity for a technically strong Data Scientist who wants to move beyond pure modelling and work on real-world solutions that go live, scale and create visible business impact. You’ll join a business investing seriously in data and AI, with the chance to work on meaningful use cases, broaden your engineering depth, and build solutions that sit close to decision-making.

Closing Paragraph

If you are a builder who enjoys taking ownership from raw data to production deployment, this role offers the chance to work on high-value AI and analytics initiatives in a complex, fast-evolving environment. It is a strong opportunity to step into a visible position where your technical capability, commercial thinking and delivery mindset will all matter.

If you want, I can also turn this into a polished DOCX format with cleaner spacing and recruiter-friendly layout.