Job highlights

- Bachelors or Masters degree in computer science,Data Science,Information Technology,or a related technical discipline
- Deep learning and Generative AI: hands-on experience with PyTorch or TensorFlow,LLM application patterns,retrieval-augmented generation,prompt engineering,model fine-tuning,evaluation,guardrails,and agentic workflows
- . Domain Experience
- . Valuable to Have

Job Summary We are seeking an exceptional Senior Data Scientist to join Baxter's Enterprise Data Analytics team. This strategic role will help transform business functions through advanced analytics, machine learning, Generative AI, Agentic AI, and data-driven decision-making.

As a senior member of the Enterprise Data Science team, you will lead the end-to-end design, development, deployment, and adoption of scalable data science and AI solutions across multiple business domains. You will partner with cross-functional stakeholders to frame high-value opportunities, translate business needs into analytical solutions, and deliver measurable outcomes while promoting responsible AI, engineering excellence, and reusable capabilities.

Responsibilities

- Lead the end-to-end lifecycle of advanced analytics, machine learning, Generative AI, and Agentic AI solutions-from opportunity identification and experimentation through production deployment, monitoring, and continuous improvement.
- Translate ambiguous business problems into well-defined data science hypotheses, solution designs, success metrics, and scalable implementation roadmaps.
- Develop and maintain advanced predictive /forecasting models for sales, revenue, expense management, and cash-flow forecasting.
- Design, architect, train, and deploy advanced machine-learning frameworks and traditional time-series models to improve forecast accuracy for sales, revenue, operating expenses, and cash flow.
- Own model validation, explainability, reliability, documentation, monitoring,



and governance in line with responsible AI and regulatory expectations.
- Provide technical leadership through architecture reviews, code reviews, mentoring, standards definition, and hands-on resolution of complex analytical challenges.
- Communicate analytical findings, business implications, risks, and recommendations clearly to senior leaders and non-technical stakeholders.
- Foster a mindset of active learning and innovation by exploring emerging AI technologies and adopting solutions that deliver business value.

Qualifications

- Bachelor's or Master's degree in computer science, Data Science, Information Technology, or a related technical discipline.
- Minimum 8 years of progressive experience in data science, machine learning, or applied AI, including experience leading complex enterprise initiatives.
- Strong hands-on record of designing, developing, and deploying production-grade machine learning, Generative AI, or Agentic AI solutions.
- Experience working with large, complex, and distributed datasets in enterprise environments.

Core Technical Skills

- Programming and distributed computing: advanced proficiency in Python, SQL, and PySpark, with strong expertise in relevant libraries such as pandas, NumPy, scikit-learn, and NLP libraries.
- Machine learning and statistics: regression, classification, clustering, forecasting, time-series analysis, experimentation, optimization, ensemble methods, and model evaluation.
- Deep learning and Generative AI: hands-on experience with PyTorch or TensorFlow, LLM application patterns, retrieval-augmented generation,



prompt engineering, model fine-tuning, evaluation, guardrails, and agentic workflows.
- Hands-on experience building autonomous AI agents and implementing the Model Context Protocol (MCP) or comparable tool-integration patterns.
- Cloud and AI platforms: strong hands-on experience with AWS machine-learning services, including Amazon SageMaker and Amazon Bedrock.
- ML engineering and operationalization: experience packaging, deploying, monitoring, and maintaining production models and AI services using APIs, containers, model registries, automated testing, observability, and retraining workflows.

Domain Experience

- Experience applying data science and AI across Finance, FinTech, Sales, Supply Chain, and Healthcare; ability to transfer methods and insights across domains is essential.
- Ability to explain complex machine-learning outputs, financial drivers, and business implications to cross-functional teams and stakeholders without black box ambiguity.

Good to Have

- Experience in healthcare, commercial, supply chain, or financial analytics environments.
- Good understanding of statistical forecasting methodologies, time-series analysis, and error metric evaluations.
- Experience working in Agile delivery models and contributing to product-oriented data science teams.
- Strong business acumen and the ability to translate technical concepts into clear decisions, trade-offs, and outcomes for non-technical stakeholders.
- Certifications: An industry-recognized professional certification in data science or AI, or completion of a top-tier data science executive program, is a strong advantage.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr. Data Scientist (Gurugram)
🏢 BAXTER
📍 Gurugram