Data Scientist

POSITION SUMMARY

Zoetis, Inc. is the world's largest producer of medicine and vaccinations for pets and livestock.

Join us at Zoetis India Capability Center (ZICC) in Hyderabad, where innovation meets excellence. As part of the world's leading animal healthcare company, ZICC is at the forefront of drivingtransformative advancements and applying technology to solve the most complex problems. Our mission is to ensure sustainable growth and maintain a competitive edge for Zoetis globally by leveraging the exceptional talent in India. At ZICC, you'll be part of a dynamic team that partners with colleagues worldwide, embodying the true spirit of One Zoetis. Together, we ensure seamlessintegration and collaboration, fostering an environment where your contributions can make a real impact. Be a part of our journey to pioneer innovation and drive the future of animal healthcare.

At Zoetis India Capability Center (ZICC) in Hyderabad, we are advancing applied AI research— turning cutting-edge AI methods into production-grade solutions. As an Applied AI Scientist, you will bridge the gap between research and application by developing, fine-tuning, and deploying AI models for real-world use cases such as intelligent assistants, predictive analytics, and process automation. You will work at the intersection of machine learning research, software engineering, and domain expertise—collaborating with global teams to design AI systems that deliver measurable business value and enhance the quality of life for animals and the people who care for them.

POSITION RESPONSIBILITIES

  Percent of Time

AI Model Development & Integration:

Research, design, and implement AI/ML models, with a focus on statistical process control, anomaly detection, process optimization, LLMs, NLP, and RAG pipelines

Fine-tune and adapt pre-trained models for Zoetis-specific use cases.

Integrate AI models into production environments using APIs and scalable back-end architectures.

Work with IoT data streams from automation equipment to enable real-time analytics and decision support.

Experimentation & Problem-Solving:

Conduct experiments to evaluate model performance, interpretability, and robustness.

Apply strong problem-solving skills to address complex AI challenges in real-world settings. Troubleshoot model integration issues and optimize performance to meet SLA guidelines.

Deployment & Maintenance:

Deploy AI models and services to production using MLOps best practices.

Monitor AI system performance, ensuring scalability, reliability, and compliance.

Implement continuous improvements based on business needs and user feedback.

Collaboration & Knowledge Sharing:

Work closely with data scientists, engineers, and product managers to align AI solutions with business objectives.

Participate in peer reviews of code, model architectures, and experimental design.

Present research findings, prototypes, and AI strategy recommendations to stakeholders.

Continuous Learning & Innovation:

Stay current with advancements in AI/ML research, frameworks, and tools.

Experiment with emerging AI technologies to explore innovative applications for Zoetis.

Mentor junior team members on AI development and applied research best practices

Research, prototype, and evaluate AI/ML models for Zoetis use cases 30%

Fine-tune LLMs and implement RAG pipelines for domain-specific applications 25%

Collaborate with engineers to integrate AI models into scalable production systems 20%

Conduct experiments, benchmark models, and analyze performance metrics 15%

Publish internal research findings, present solutions, and contribute to AI strategy 10%

ORGANIZATIONAL RELATIONSHIPS
Partner with business leaders to identify opportunities for AI adoption and automation.

Work closely with data scientists to integrate models into production environments.

Coordinate with DevOps, cloud engineering, and security teams to ensure AI applications meet compliance and performance standards.

Mentor and guide junior engineers to upskill in AI and full-stack development.

EDUCATION AND EXPERIENCE
Master's degree in computer science, engineering, physics or related field.

Experience:

• 1.5–6.5 years of hands-on experience in AI/ML, with a strong track record of developing and deploying analytics solutions in production environments

• Advanced proficiency in Python (PyTorch, TensorFlow, machine learning libraries), R (tidyverse, shiny, statistical/machine learning packages), and SQL for data analysis, modeling, and visualization

• Applied expertise in statistical process control (SPC), design of experiments (DOE), Six Sigma methodologies, and root cause analysis to drive process improvement and quality assurance

• Familiarity with manufacturing data sources such as SCADA platforms, PLCs, and data historians and analysis concepts from these data streams (IoT, time series analysis, real-time data processing, and multivariate process monitoring for manufacturing applications)

• Proficient in MLOps practices for deploying, monitoring, and maintaining models in production environments

• Experience with cloud-based AI/ML services (AWS Sagemaker, Azure ML, GCP Vertex AI)

• Skilled in source control and collaborative development using Git and GitHub Enterprise

• Familiarity with natural language processing (NLP), large language model (LLM) fine-tuning, retrieval-augmented generation (RAG) architectures, and vector databases

TECHNICAL SKILLS REQUIREMENTS

Indicate the technical skills required and/or preferred, as applicable.

· ML/AI Frameworks: PyTorch, TensorFlow, Hugging Face Transformers.

· EDA/Visualization frameworks: rmarkdown, ggplot2, shiny

· Specialization Areas: SPC, DOE, Anomaly detection, NLP, LLM fine-tuning, Prompt Engineering, RAG pipelines.

· Data Tools: Pandas, NumPy, Scikit-learn, tidyverse, Vector Databases (Pinecone, Weaviate, FAISS).

· Databases: PostgreSQL, SQL, NoSQL.

· Programming Languages: Python, SQL, optionally C++/Rust for performance-criticalcomponents.

PHYSICAL POSITION REQUIREMENTS

Regular working hours are from 11 AM to 8:00 PM IST.

Sometimes, more overlap with the EST Time zone is required during production go-live.

Full time