Principal Data Scientist

Role: Principal Data Scientist – Agentic AI & Decision Systems

Function: Data Science / Applied Machine Learning

Location: Bangalore

Type: Full-time

Industry: Artificial Intelligence, Commerce, Payments, Logistics, SMB/MSME Technology

About Company

The company is building a foundational AI agent platform at India scale. It operates across commerce, payments, and logistics for SMBs, SMEs, and MSMEs.

The platform interprets intent through voice, language, and contextual signals, then autonomously executes outcomes across physical and digital systems. It eliminates the need for dashboards, complex integrations, or manual workflows.

Backed by large-scale national infrastructure and distribution, the company is deploying agentic AI responsibly to bring millions of small businesses into the AI-enabled economy. The team operates with a 0→1 startup mindset—lean, high-ownership, and focused on durable system design over short-term metrics.

Position Overview

This is not a traditional data science role. As Principal Data Scientist, you will translate complex, ambiguous real-world problems into production-grade intelligent systems that don't just predict—but decide and act. You will operate at the intersection of machine learning, optimization, and agentic architectures across pricing, supply, routing, forecasting, and allocation domains. This is a high-ownership, high-impact role building 0→1 decision systems at India scale.

Role & Responsibilities

• Own end-to-end problem definition and solution design for high-impact domains including pricing, demand forecasting, routing, and resource allocation
• Design and build production-grade decision systems that combine ML models, optimization solvers, and agentic workflows
• Deploy and manage models using TorchServe or TensorFlow Serving on AWS infrastructure for high-throughput inference
• Architect evaluation frameworks, feedback loops, and monitoring systems to ensure model reliability in production
• Drive the full lifecycle from raw data to deployed, measurable system—collaborating closely with engineering for productionization
• Mentor and grow junior and mid-level data scientists, conducting design reviews and raising the technical bar
• Engage directly with business and product stakeholders to translate operational complexity into tractable ML and optimization problems

Must Have Criteria

• 8–15 years of hands-on Data Science or Applied ML experience, with at least 3 years in a principal or staff-level individual contributor role
• Demonstrated track record of building and shipping production-grade ML systems end-to-end (not just notebooks or prototypes)
• Strong proficiency in Python and PySpark for large-scale data processing and model development
• Experience deploying and serving ML models using TorchServe or TensorFlow Serving on AWS (EC2, SageMaker, or EKS)
• Experience with optimization techniques (linear programming, combinatorial optimization, or constraint-based solvers) applied to real-world problems
• Hands-on experience with experimentation design and causal inference methods (A/B testing, diff-in-diff, or instrumental variables)
• Prior work in at least one of: pricing systems, demand/supply forecasting, logistics routing, or resource allocation in a production environment

Nice to Have

• Experience designing or building agentic AI systems, LLM-based workflows, or multi-step autonomous decision pipelines
• Background in marketplace, logistics, fintech, or e-commerce platforms operating at national or regional scale
• Experience with reinforcement learning or multi-armed bandit systems applied to real-world decision problems
• Prior experience in a 0→1 startup or platform build where you defined the ML architecture from scratch
• Familiarity with MLflow or Kubeflow for ML lifecycle management and orchestration in production

What We Offer

• Opportunity to architect foundational AI decision systems with national-scale impact across India's SMB economy
• High ownership and autonomy—define the problem, own the system, measure the outcome
• Work directly with the company's infrastructure and distribution to deploy agentic AI responsibly at scale
• Lean, high-caliber team with a 0→1 startup culture backed by large-scale organizational support