Job Opportunities API

Location, Mexico City, Mexico **This 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. Requirements Experience in life sciences, pharma, biotech, or regulated manufacturing industries. Proven hands-on experience delivering data science projects end-to-end, in an industry or consulting setting — not just academic or research contexts. Strong proficiency in Python and the data science stack: Jupyter, Pandas, scikit-learn, and relevant ML libraries. Solid grounding in statistics and machine learning: model development, validation, and interpretation. Experience analyzing large, complex, or unstructured datasets and communicating insights effectively. Familiarity with software development best practices: Git, Docker, cloud platforms (AWS preferred), and relational databases (e.g., PostgreSQL). Strong communication skills in English and Spanish (written and verbal); ability to work effectively with global teams. Ability to manage multiple projects simultaneously and adapt in a dynamic, client-driven environment. Preferred Background in optimization techniques (constrained, combinatorial, convex, discrete). Experience with Bayesian methods and experimental design. Familiarity with anomaly detection, process monitoring, or quality control applications. Exposure to bioinformatics or scientific data pipelines. Hands-on experience with Generative AI tools and LLM-based workflows. Advanced degree (Master's