Lead ML research, model development and optimization — building predictive analytics, forecasting and classification/regression models for clients across sectors (fintech, healthcare, e-commerce, sustainability).
Engage in generative-AI / LLM fine-tuning and deployment where required (prompt engineering, model selection, data preparation, inference pipelines).
Architect and manage robust data-engineering / ETL pipelines with data ingestion, cleaning, transformation, and integration; ensure pipelines scale and comply with data-governance / GDPR standards.
Oversee MLOps and cloud deployment: containerization (Docker, Kubernetes), CI/CD workflows, model monitoring, version control, production deployment and maintenance.
Mentor mid-level and junior engineers / data scientists; review code and model outputs; guide best practices and documentation.
Serve as main technical contact for clients; collaborate to translate business requirements into data solutions; present findings, explain model outcomes, support decision-making.
Required Skills & Qualifications
Strong proficiency in Python (data/ML), with experience in ML libraries/frameworks (e.g. scikit-learn, TensorFlow or PyTorch) and familiarity with data-engineering tooling.
Solid experience with data engineering / ETL pipelines, cloud platforms (AWS, Azure, or GCP), data storing/processing and cloud-native analytics.
Familiarity with MLOps, containerization (Docker), orchestration (Kubernetes), CI/CD and deploying ML models in production.
Experience or working knowledge in generative-AI / LLM fine-tuning / NLP — or strong interest and ability to learn quickly.
Knowledge of data governance and privacy compliance (GDPR-aligned workflows) a plus.
Excellent communication, remote-team collaboration, self-management, adaptability and comfort working across time-zones.
Bachelor’s or Master’s degree (or equivalent) in Computer Science, Data Science, Statistics, Mathematics, or a related field.
Minimum 5–7 years of professional experience in data science / ML / data engineering.
