Data Science and Business Intelligence Professional III

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

Key Responsibilities

1. Predictive Modeling & Advanced Analytics

• Design, build, validate, and deploy predictive models that improve business performance.

• Develop scoring models, forecasting engines, propensity models, anomaly detection systems, and optimization solutions.

• 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

2. 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

3. 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.

4. 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

5. 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 frameworks

• Prompt systems

• Workflow automation tools

• Evaluation frameworks

MLOps / Production Readiness

• Model deployment concepts

• Monitoring drift and performance

• Retraining lifecycle

• Governance and documentation

• CI/CD awareness for data science workflows

Business & Communication Skills

• Excellent spoken and written English.

• Ability to explain technical concepts to senior business leaders.

• Strong consulting mindset — solve the real problem, not only the stated request.

• Ability to influence through insight and credibility.

Qualifications

• Bachelor’s or Master’s Degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, Economics, or related field.

• 5+ years of experience in Data Science / Advanced Analytics / Machine Learning roles.

• Experience delivering measurable business outcomes.

• Enterprise / global environment experience preferred.

Preferred Technology Environment

• Google Cloud Platform

• BigQuery

• Vertex AI

• Google Workspace

• Looker / Looker Studio

• OpenAI / Anthropic / Gemini / Open-source LLM ecosystems

• Automation / orchestration platforms

Success Measures

The successful candidate will help deliver:

• Higher model adoption

• Revenue / cost / productivity impact

• Faster time-to-insight

• Strong predictive performance where relevant

• Scalable AI workflows

• Reduced manual effort through automation

• Trusted stakeholder partnerships

• Practical use of next-generation AI

Why Join

This is an opportunity to build enterprise-grade AI solutions from Bengaluru for a global business environment and help shape the next generation of analytics, automation, and intelligent decision systems.

Category: Technology