Senior Data Scientist

About the role

You will architect and deploy production-grade AI models that power Resilinc's agentic supply chain intelligence platform, directly shaping how global enterprises predict and mitigate risk. In this role, you will own the end-to-end lifecycle of high-impact data science initiatives, from translating ambiguous customer problems into scalable analytical solutions to validating model performance in live environments. You will collaborate closely with engineering, product, and customer success teams to ensure that your models are not only accurate but also explainable, robust, and seamlessly integrated into the Resilinc platform. You will act as a key technical advisor to enterprise customers, helping them interpret model outputs and align AI capabilities with strategic business objectives. This is an opportunity to work on meaningful, real-world problems where your work directly contributes to the resilience of critical supply chains for life sciences, aerospace, and other essential industries. You will stay at the forefront of AI innovation, applying and advancing state-of-the-art techniques in natural language processing, time-series forecasting, and probabilistic modeling to navigate complex global risks. You will mentor junior data scientists and analysts, fostering a culture of technical excellence, rigorous experimentation, and data-driven decision-making across the organization. Ultimately, you will be a driving force behind the product roadmap for our AI offerings, ensuring that Resilinc remains a leader in autonomous supply chain risk management.

Key facts

What you'll do

Design, develop, and deploy scalable machine learning and deep learning models that process massive, multi-modal supply chain datasets to power real-time risk detection and decision automation.

Partner with Customer Success and Product teams to conduct discovery sessions, define success metrics, and translate complex enterprise requirements into concrete model objectives and evaluation frameworks.

Lead the development of our core AI agents, including prompt engineering, tool integration, and orchestration strategies that enable autonomous problem-solving across the supply chain network.

Build and maintain robust MLOps pipelines and experiment tracking systems to ensure reproducibility, scalability, and efficient deployment of models into production environments.

Perform advanced statistical analysis and exploratory data analysis on diverse, noisy datasets to uncover hidden patterns, anomalies, and drivers of supply chain disruption.

Create clear, compelling data visualizations and narrative reports that communicate model behavior, performance trends, and business impact to both technical and non-technical stakeholders.

Collaborate with our Engineering team to optimize data ingestion, storage, and processing workflows, ensuring that the data infrastructure can support high-frequency model inference and learning.

Contribute to the definition of our technical AI strategy, evaluating emerging open-source frameworks and cloud services to maintain a competitive edge in model performance and efficiency.

Work closely with domain experts to validate model outputs against real-world logistics, manufacturing, and regulatory constraints, ensuring solutions are practical and actionable.

Mentor and guide junior data scientists and analysts, reviewing code, providing feedback, and promoting best practices in software engineering, testing, and documentation.

Requirements

Must have a Master's or PhD degree in Computer Science, Statistics, Mathematics, or a closely related quantitative field, demonstrating a rigorous foundation in data science theory and practice.

Must possess 8+ years of hands-on experience in data science or machine learning roles, with a proven track record of delivering production-grade AI solutions.

Must be highly proficient in Python and its data science ecosystem, including libraries such as pandas, NumPy, scikit-learn, and must have experience with modern deep learning frameworks like PyTorch or TensorFlow.

Must have extensive experience with MLOps tools and cloud platforms, including but not limited to Docker, Kubernetes, AWS, Azure, or GCP, for building and deploying scalable AI systems.

Must have a strong background in statistical modeling, experimental design (A/B testing), and performance evaluation metrics relevant to classification, regression, and time-series forecasting.

Must have exceptional verbal and written communication skills, with the ability to explain complex technical concepts clearly to non-technical stakeholders and C-level executives.

Must be a self-motivated generalist who thrives in a fast-paced, ambiguous environment, taking ownership of problems and driving them to completion with minimal supervision.

Must be comfortable working full-time hours within India Standard Time (IST) and available for synchronous collaboration during core business hours as needed.

Nice to have

Experience with natural language processing (NLP) and large language models (LLMs) for extracting insights from unstructured text such as news, supplier communications, and internal reports.

Experience building and deploying AI agents or working with agentic AI frameworks that enable autonomous task execution.

Experience in the life sciences, pharmaceutical, aerospace, defense, or high-tech manufacturing sectors, providing domain context for supply chain risk scenarios.

Practical notes

This is a fully remote position based in India; candidates must be legally authorized to work in India without sponsorship.

All collaboration is asynchronous and synchronous virtual; in-person meetings, travel, or visa requirements do not apply to this role.