Senior Data Scientist Builder

About the role

Puma Energy's Data & Analytics team is building a modern cloud-native data platform on AWS and Databricks. It is looking for a Senior Data Scientist who is fundamentally a builder: someone who personally designs, ships and owns machine learning and agentic automation systems in production, and who raises the technical bar for the wider team through hands-on mentorship. The role partners closely with the Data Engineering function and with commercial, retail, supply and trading stakeholders.

Key responsibilities

• Own data science products end-to-end, from framing a business problem to shipping and monitoring a production model.
• Build and deploy applied agentic automation solutions that augment or replace manual analytical and operational workflows.
• Guide teams on modeling technique, architecture and productionization strategy, without formal people management.
• Translate complex business problems into scalable, measurable data science and automation solutions.
• Raise the bar on experimentation rigor, reproducibility and production discipline across the team.
• Build and maintain ML models and solutions using Databricks, Spark (PySpark), Python, SQL and AWS, covering the full ML lifecycle.
• Write reusable modeling frameworks, curated feature sets and self-service data science assets.
• Evaluate and select agentic frameworks, and set guardrails, evaluation and monitoring for agentic systems.
• Mentor other data scientists and engineers through design reviews, pairing and code review.
• Implement automated model validation, monitoring and retraining pipelines with Data Engineering and MLOps, and troubleshoot production issues.

Required profile

• 6-10 years of hands-on experience in data science / machine learning, with a track record of production ML solutions.
• Strong, demonstrable hands-on experience with Databricks (non-negotiable).
• At least 1 year of hands-on experience building applied agentic automation solutions deployed or piloted in a real business context.
• Excellent communication skills, able to mentor peers and influence stakeholders.

Required skills

• Python, SQL and at least one ML framework (TensorFlow, PyTorch or scikit-learn)
• Apache Spark (PySpark) and AWS
• Git, CI/CD, testing and code quality
• Nice to have: Databricks or AWS certifications, MLOps tooling (MLflow, Airflow/Prefect) and Terraform, experimentation/causal inference and A/B testing, domain exposure to energy or trading analytics, Power BI/Tableau/Plotly-Dash