Apply machine learning, statistics, experimentation frameworks, and advanced analytics to improve decisions.
Translate enterprise data into forward-looking business intelligence.
Typical Use Cases:
Churn prediction
Collections prioritization
Dispute risk scoring
Cross-sell / upsell propensity
Lead prioritization
Customer lifetime value
Revenue leakage detection
Operational SLA risk prediction
Generative AI & Agentic Solutions
Design and implement intelligent solutions using LLMs, copilots, and agentic workflows.
Build AI assistants that reason, retrieve knowledge, summarize, orchestrate tasks, and support operational teams.
Develop multi-step AI workflows using APIs, tools, orchestration platforms, and enterprise systems.
Evaluate where autonomous vs human-in-the-loop models are appropriate.
Required Understanding:
Prompt engineering
RAG architectures
Tool-use frameworks
Memory patterns
Multi-agent orchestration
Workflow automation with LLMs
Guardrails and safety layers
AI observability and quality monitoring
AI Economics & Model Efficiency
Understand commercial trade-offs of model usage including token cost, latency, retrieval overhead, orchestration cost, and infrastructure efficiency.
Recommend the right model for the right use case based on quality, speed, and cost.
Optimize prompts, routing logic, and workflows for scalable enterprise deployment.
Evaluate open-source, hosted, and cloud-native model options.
Business Domain Intelligence
Use data science to solve high-value problems across:
Finance
Collections optimization
Payment behavior modeling
Cash forecasting
Bad debt prediction
Profitability analytics
Customer Experience
Sentiment analysis
Case prioritization
Complaint prediction
Quality intelligence
Retention risk scoring
Sales / Commercial
Opportunity scoring
Cross-sell targeting
Pricing intelligence
Pipeline risk prediction
Operations
Volume forecasting
Capacity planning
Service failure prediction
Turnaround optimization
Visualization & Storytelling
Present insights through dashboards, scorecards, simulations, and executive-ready narratives.
Use Tableau, Power BI, Looker, or Looker Studio.
Convert model outputs into clear business actions.
Required Skills
Programming & Data
Strong Python expertise
Strong SQL expertise
Data wrangling and feature engineering
API integrations
ETL / ELT familiarity
Statistical computing
Machine Learning
Regression / Classification
Tree-based methods
Ensemble learning
Clustering
NLP
Time series forecasting
Recommender systems
Optimization techniques
Modern AI Stack
Working knowledge of:
Vertex AI
BigQuery
LLM APIs and model providers
Embeddings / vector search
Agent fr
Additional Information
At Iron Mountain we know that work, when done well, makes a positive impact for our customers, our employees, and our planet. That's why we need smart, committed people to join us. Whether you're looking to start your career or make a change, talk to us and see how you can elevate the power of your work at Iron Mountain.
We provide expert, sustainable solutions in records and information management, digital transformation services, data centers, asset lifecycle management, and fine art storage, handling, and logistics. We proudly partner every day with our 225,000 customers around the world to preserve their invaluable artifacts, extract more from their inventory, and protect their data privacy in innovative and socially responsible ways.
Are you curious about being part of our growth story while evolving your skills in a culture that will welcome your unique contributions? If so, let's start the conversation.
Job Description
Senior Data Scientist - AI, Predictive Intelligence & Agentic Solutions
Location: Bengaluru, India
Work Model: Hybrid - Minimum 3 Days per Week in Office
Role Summary
We are looking for a modern, business-facing Senior Data Scientist who can combine predictive analytics, machine learning, Generative AI, agentic architectures, and decision intelligence to solve enterprise problems at scale.
This role requires someone who can move seamlessly between business priorities and technical execution - designing models, scoring engines, forecasting solutions, LLM-powered workflows, and intelligent systems that create measurable outcomes.
The ideal candidate understands that modern data science is no longer only about model accuracy. It is about:
Faster decision-making
Embedded intelligence in workflows
Production-ready AI systems
Commercial value creation
Cost-efficient model usage
Trusted and governed AI adoption
This role will support global enterprise functions including:
Finance
Customer Experience
Sales / Commercial
Operations
Ironmountain · India