Data Scientist - Life Sciences AI / ML

Location, Mexico City, MexicoThis role will require regular presence (2-3 times / week) at our office in Mexico City,

We are looking for a Data Scientist who brings strong hands-on experience applying machine learning, statistical modeling, and optimization techniques to real-world problems — preferably in life sciences, pharma, biotech, or regulated manufacturing environments.

You will work directly with clients, embedded in their scientific and technical teams, owning the full delivery lifecycle: from understanding the problem and acquiring data, through modeling and validation, to deployment and monitoring. You will also contribute to Zifo's internal AI/ML practice, helping shape how we deliver data science across the region.

This is a high-impact, client-facing individual contributor role. The right candidate combines computational rigor with the communication and consulting skills needed to earn trust with technical and non-technical stakeholders alike.

What You Will Do

• Model development: Design, train, and tune machine learning models (supervised and unsupervised) and statistical algorithms to solve client challenges — including anomaly detection, process optimization, batch scheduling, and quality control.

• Optimization: Apply constrained optimization, combinatorial methods, and Monte Carlo simulations to complex operational and manufacturing problems.

• Data analysis: Collect, clean, and analyze large, complex, and often unstructured datasets; translate findings into clear, actionable insights for client stakeholders.
• Statistical modeling: Apply statistical techniques including Bayesian experimental design, feature engineering, and uncertainty quantification to scientific and engineering data.
• Monitoring & alerting: Implement real-time monitoring of data streams and system logs; tune detection thresholds to minimize false positives and surface meaningful signals.
• Cross-functional collaboration: Partner with data engineers, software engineers, scientists, and business stakeholders across the full project lifecycle — from scoping through deployment.
• Generative AI integration: Explore and apply GenAI capabilities to accelerate experimentation, enhance modeling workflows, and unlock efficiencies for clients.
• Best practices: Champion software engineering and data science best practices: reproducibility, version control, documentation, and scalable solution design.
• Communication: Communicate complex analytical findings clearly — in writing and in meetings — to both technical and non-technical audiences, including client leadership.