Role Description Nuvation Bio is looking for a highly motivated and productive Principal Scientist to join our Research Department supporting our drug discovery and development programs. The Principal Scientist will contribute to research for multiple oncology discovery programs. They will initiate, direct, and manage external and internal experimental studies that are critical to advancement of programs, informing corporate strategy, and working to serve the unmet medical needs in oncology. You will be responsible for: - Support the research strategy for multiple oncology programs by designing and executing critical and relevant experimental studies that further the understanding of Nuvation Bio compounds. - Responsible for study planning, protocol design, study execution, and data analysis of pharmacology and mechanism of action studies. - Manage outsourced studies with external vendors/CROs/academic collaborators. - Support medicinal chemists by providing data critical to understand SAR/biochemical characterization of early-stage programs. - Work cross-functionally with members of the broader research team and effectively collaborate with a fully integrated team to facilitate the success of Nuvation projects. - Build our reputation in the scientific community with conference presentations and publications. - All other duties as assigned. Qualifications - PhD in relevant biological discipline with a minimum of 5+ years’ experience preferred, or MS with 7+ years, Bachelor’s degree with 10+ years of relevant pharmaceutical industry experience in a fast-paced environment. - Demonstrated and broad expertise in cancer cell biology with knowledge of relevant experimental techniques, including in vitro and in vivo oncology model systems. - Demonstrated expertise in the evaluation and development of small molecules. - Strong track record in oncology demonstrating knowledge of multiple fields of biology, reflected in multiple successful projects and publications. - Exceptional problem solving, analytical thinking, and strategic skills. - Excellent written and verbal communication skills with the proven ability to work effectively as a member of a multidisciplinary team. Requirements - Problem-Solver: As an action-oriented self-starter, you’re eager to apply your expertise in diagnosing inefficiencies, determining root causes, and recommending solutions to complex challenges. - Personable: You establish and nurture highly effective relationships with colleagues and key stakeholders that support and advance project goals and objectives. - Credible: Thorough understanding of clinical methodologies and providing sound analytical support, you quickly establish credibility as a reliable resource. - Emotional intelligence, curiosity, and a knack to figure out a way to build something better. - Communication: Effectively listens, conveys, and receives ideas. Maintains a consistent flow of information. A strong presenter and effective communicator with the ability to influence stakeholders. - Accountability: Acts with a clear sense of ownership - whether the task is in their role description. Holds yourself and others accountable to execute all tasks at hand through seen and unforeseen circumstances with high quality and integrity. - Adaptability: Can manage in an environment of ambiguity. Embraces the change of a new system, process, technology, or idea in the organization. You could reassess priorities, alter goals and take risks. Benefits - Competitive Base Salary, Bonus, and Equity Plans. - Unlimited Vacation and 10 Sick Days Annually. - Excellent Medical, Dental, and Vision Coverage. - 401K with Company Matching. - And much more!

Role Description The ML Engineer builds the classical retail-ML cores that power the highest-stakes agents on an AI-native retail decisioning platform — demand forecasting that must beat a legacy system, replenishment and allocation models, causal-insight models for executive narratives, and pricing / promotion / markdown / assortment models. The role consumes the enterprise MLOps platform (model registry, drift detection, feature store, library wrappers) and contributes use-case-specific implementations. Remote candidates outside of Thailand are welcome to apply. Key Responsibilities - Build, train, evaluate, and deploy classical retail ML models — forecasting, replenishment, allocation, causal inference (DoWhy / EconML), pricing elasticity, promotion lift, markdown optimisation, assortment. - Use company-curated classical ML wrappers (Prophet, statsmodels, DoWhy / EconML, LightFM, scikit-learn, XGBoost, LightGBM) — do not rebuild open-source libraries from scratch. - Author per-model evaluation methodology appropriate to each model class (forecast MAPE, classification accuracy / precision / recall, causal precision). - Register every model in the enterprise Model Registry with model cards; configure drift-detection thresholds; use the enterprise Feature Store for shared features. - Beat a legacy forecasting system by a measurable margin (MAPE improvement) and document evidence for trust-gate progression alongside the legacy run. - Build causal models for executive-insight agents using DoWhy or EconML; document causal assumptions; ensure mandatory citations for narrative outputs. - Partner with AI Engineers on ML model ↔ agent integration (invocation contracts, latency budgets, fallback behaviour); co-design HITL gate criteria for ML-heavy agents. - Partner with Suite Product Owners on BU adoption, gate criteria, success metrics; document per-model business value (forecast accuracy → inventory savings, replen accuracy → stock-out reduction). Qualifications - Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or a related discipline. - 5+ years building production ML systems with retail or commercial decisioning models (forecasting, replenishment, pricing, recommendation, or comparable). - Strong Python and Spark / PySpark; SQL fluency. - MLOps consumer experience — has registered models, configured drift, used a feature store. - Cloud + Databricks (or equivalent lakehouse) production experience; Azure preferred. - Causal inference exposure (DoWhy / EconML). - Eval discipline — knows how to design appropriate evals per model class. - Retail / commerce domain fluency or rapid acquisition. Preferred Qualifications - Retail forecasting at multi-store / multi-SKU scale; promotional lift / markdown optimisation in production. - Causal inference in commercial decisioning; replenishment / allocation algorithms. - Online learning / near-real-time inference. - Vendor certifications such as Databricks Machine Learning Professional or Azure AI Engineer Associate.

• Architecture Design • Data-plane: high-parallel crawler, session management, anti-bot bypass, CAPTCHA solver • Control-plane: API, authorization, billing, quotas, monitoring • Infrastructure: microservices, Docker/K8s, CI/CD, observability • Development Ownership • Establish development and release processes • Code quality control and technical decision oversight • Anti-Bot Research & Bypass • Browser and network-level fingerprinting (JA3, TLS, CDP) • Protection bypass: Cloudflare, Akamai, Qrator, ServicePipe, Variti, and others • DevOps / SRE • CI/CD setup, Helm charts, scaling configuration • Monitoring organization (Prometheus, Grafana, Loki) and SLA processes

- Architecture Design - Data-plane: high-parallel crawler, session management, anti-bot bypass, CAPTCHA solver - Control-plane: API, authorization, billing, quotas, monitoring - Infrastructure: microservices, Docker/K8s, CI/CD, observability - Development Ownership - Establish development and release processes - Code quality control and technical decision oversight - Anti-Bot Research & Bypass - Browser and network-level fingerprinting (JA3, TLS, CDP) - Protection bypass: Cloudflare, Akamai, Qrator, ServicePipe, Variti, and others - DevOps / SRE - CI/CD setup, Helm charts, scaling configuration - Monitoring organization (Prometheus, Grafana, Loki) and SLA processes