About the role

Job Title: Senior Associate, Analytics

Experience: 6-8 Yrs

Location: Hyderabad and Bangalore

Company Overview:

Ensemble Health Partners India is at the forefront of innovation in the Revenue Cycle Management (RCM) space, leveraging modern technology to drive meaningful, real‑world impact. Our future‑ready platforms bring together AI‑driven analytics , intelligent data ingestion, workflow automation, and business intelligence built on a scalable, cloud‑native architecture.

Our AI‑powered solutions are actively running in production, continuously optimizing processes and delivering data‑driven insights at scale. With the second‑largest market share in the U.S. RCM industry, a global workforce of 15,000+ professionals, and 12 technology patents , we deliver results through strong teams, proven processes, and flexible, modern technologies.

As part of our continued growth, we have launched our Global Capability Center (GCC) in Hyderabad designed to serve as a strategic extension of our global operations. The GCC brings together technology, analytics, and RCM expertise to build scalable solutions, accelerate innovation, and support our long‑term vision of transforming healthcare operations.

At Ensemble Health Partners India, we foster a culture of growth, collaboration, and innovation where your expertise is valued, your ideas are heard, and your work makes a measurable impact.

Position Summary:

Ensemble Health Partners is seeking a talented Senior Associate, Analytics to join our AI Innovation group. This is an individual contributor role focused on designing, developing, and delivering high-quality analytical solutions, machine learning models,

and intelligent automation workflows. The Senior Associate operates with meaningful autonomy on assigned workstreams, executing against well-defined technical objectives while growing toward broader technical ownership. The ideal candidate combines solid engineering fundamentals with genuine curiosity about applied AI, a commitment to code quality, and the drive to deliver solutions that create measurable business value in healthcare revenue cycle management

Key Responsibilities:

Ensemble Health Partners’ AI Innovation group is the technical engine powering next-generation revenue cycle management solutions. Our team builds production-grade AI and ML systems that automate complex workflows, reduce operational friction, and deliver measurable outcomes for healthcare providers nationwide. We operate at the intersection of cutting-edge AI technology and real-world healthcare operations, with an uncompromising commitment to engineering excellence, Clean Architecture, and responsible AI.

2.1 Team Structure

The AI Innovation group is organized around complementary technical leadership and execution roles. Senior Associates are the

primary delivery contributors within each technical lead’s workstream:

3 WHAT YOU WILL DO

• Lead, Analytics – Architecture & Frictionless Delivery: Owns architectural standards, formal review processes, C4 modeling, and delivery of Frictionless Initiative projects that reduce revenue cycle friction

• Lead, Analytics – Code Craftsmanship, Simplification & Agentic AI: Owns engineering excellence through rigorous code review and exemplar code creation, systematic reduction of pipeline and model complexity, and development of autonomous

AI agents for revenue cycle workflows

• Senior Associate, Analytics (this role): Primary execution contributor within an assigned technical workstream, delivering

AI/ML solutions, automation workflows, and analytical capabilities under the direction and mentorship of the Lead, Analytics

• Manager, Analytics: Provides people management, project governance, compliance stewardship, and business development for the broader Analytics function

3 What You Will Do

3.1 AI/ML Model Development & Analytics

• Design, develop, and maintain machine learning models and analytical solutions aligned with assigned initiative workstreams, including the Frictionless Initiative and Agentic AI portfolio

• Perform feature engineering, model training, and iterative refinement to deliver models that meet defined performance and

business impact targets

• Evaluate model effectiveness using the right metrics for the problem at hand—classification models using precision, recall,

F1, AUC-ROC, and calibration curves; regression models using RMSE, MAE, and R2

; ranking models using NDCG and MAP—

and articulate the business implications of metric trade-offs to non-technical stakeholders

• Monitor deployed models for performance degradation, data drift, and concept drift using statistical process control techniques and automated alerting, triggering retraining or escalation when thresholds are breached

• Conduct exploratory data analysis to surface patterns, opportunities, and actionable insights from healthcare revenue cycle

data

3.2 Agentic AI & Intelligent Automation

• Build and maintain data pipelines and ETL processes that support analytical workflows and model inference in production

• Establish and track metrics that quantify the business impact of delivered solutions, including friction reduction outcomes

and automation throughput

• Ensure all models and analytical solutions are documented following team standards, including data lineage, evaluation

methodology, and operational runbooks

3.2 Agentic AI & Intelligent Automation

• Contribute to the design and implementation of agentic AI workflows that autonomously handle multi-step revenue cycle

tasks such as claims triage, denial root-cause analysis, and prior authorization follow-up

• Build and integrate automation solutions using tools such as Microsoft Copilot, Model Context Protocol (MCP), and Power

Automate to streamline operational processes and augment analyst productivity

• Implement integrations between AI agents, enterprise data sources, and downstream systems with appropriate observability, error handling, and guardrails

• Evaluate the effectiveness of LLM-based components using metrics appropriate to the task—ROUGE and BERTScore for

summarization and extraction; faithfulness, groundedness, and hallucination rate for retrieval-augmented generation; task

success rate and tool-call accuracy for agentic workflows—and apply LLM-as-judge evaluation patterns where automated

scoring at scale is required

• Prototype and evaluate emerging AI frameworks and automation technologies, preparing concise evaluation reports and

adoption recommendations for the Lead

• Write clean, well-tested, production-quality code that follows Clean Architecture principles and established team coding

standards

3 WHAT YOU WILL DO

3.3 Solution Delivery & Execution

• Deliver assigned project milestones on time and to the quality standards defined by the Lead, Analytics and Product Management

• Participate fully in Agile ceremonies including sprint planning, stand-ups, retrospectives, and backlog refinement

• Collaborate with Product Managers and operational stakeholders to translate business requirements into precise technical

solutions, proactively surfacing ambiguities and risks

• Ensure all deliverables meet defined acceptance criteria and pass quality gates before deployment to production

• Maintain accurate and current documentation for all solutions, models, pipelines, and automated processes owned

3.4 Architecture & Technical Excellence

• Follow Clean Architecture principles and established architectural patterns in all development work

• Participate in architectural reviews and contribute meaningfully to design discussions for assigned initiatives

• Adhere to C4 notation and system diagramming standards when creating or updating architecture documentation

• Identify and proactively communicate potential architectural debt or design concerns to the Lead, Analytics

• Contribute to the development of reusable patterns and components—models, pipelines, agent templates—that can be

leveraged across the broader team

• Adopt and champion code quality tooling including linting, static analysis, and automated testing frameworks as defined

by the Lead

3.5 Collaboration & Knowledge Sharing

• Collaborate effectively with peers across the AI Innovation group, including team members working across different workstreams and technical leads

• Communicate progress, risks, and blockers proactively to the Lead, Analytics and relevant stakeholders—without waiting

to be asked

• Participate in code reviews as both author and reviewer, providing constructive, specific feedback to maintain code quality

across the team

• Prepare and present technical topics, solution demonstrations, and project outcomes in team knowledge-sharing sessions

• Support onboarding of new team members and contribute to a collaborative, psychologically safe team environment

3.6 Learning & Professional Growth

• Continuously develop technical skills in AI/ML engineering, agentic AI frameworks, data engineering, Clean Architecture,

and healthcare revenue cycle domain knowledge

• Complete training and certifications as identified in the professional development plan, with emphasis on areas of active

project need

• Stay current with emerging technologies, research, and methodologies relevant to the analytics, ML engineering, and intelligent automation domains

• Actively apply learnings from mentoring sessions, architectural training, and knowledge-sharing events to daily engineering

practice

4.1 Required Qualifications

• Experience: 6–10 years of professional experience in analytics, data science, software engineering, or ML engineering

roles, with a demonstrated record of delivering production-grade solutions

• Programming: Proficient Python development with a working emphasis on code quality, testability, and maintainability;

comfort with version control workflows and collaborative development practices

• ML Engineering: Hands-on experience building, training, and deploying machine learning models in production, including

familiarity with common frameworks such as scikit-learn, XGBoost, or equivalent

• Model & LLM Evaluation: Demonstrated ability to select and apply evaluation metrics appropriate to the model type and

business context—including classification metrics (precision, recall, F1, AUC-ROC), regression metrics (RMSE, MAE, R2

), and

LLM-specific metrics (faithfulness, hallucination rate, ROUGE, BERTScore, task success rate)—and to communicate metric

trade-offs and their business implications clearly; familiarity with production monitoring for model drift and performance

degradation

• Data Engineering: Experience building and maintaining data pipelines, ETL processes, and data transformation workflows

supporting analytical or ML workloads

• Analytical Thinking: Ability to formulate and execute exploratory data analysis, communicate findings clearly, and translate

analytical insights into actionable recommendations

• Software Design: Familiarity with software design principles, modular code organization, and the basics of Clean Architecture or equivalent structured design methodologies

• Collaboration: Demonstrated ability to work effectively in a team environment, communicate technical progress clearly,

and execute reliably on assigned work commitments

4.2 Preferred Qualifications

• Experience in healthcare revenue cycle management (RCM), including familiarity with claims processing, denials management, prior authorization, or related operational workflows

• Exposure to agentic AI concepts and frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar LLMbased orchestration platforms

• Experience with cloud-based ML and data platforms such as Azure ML, Azure Data Factory, Databricks, or equivalent

• Familiarity with Microsoft Copilot, Power Automate, or Model Context Protocol (MCP) in a development or prototyping

context

• Working knowledge of CI/CD practices, automated testing frameworks, and code quality tooling such as SonarQube, Pylint,

Ruff, or equivalent

• Familiarity with Explainable AI (XAI) concepts and their application in communicating model behavior to business stakeholders

• Knowledge of AI governance and compliance considerations in regulated industries, including basic awareness of NIST AI

RMF or HITRUST frameworks

• Experience with C4 model notation or equivalent structured diagramming approaches for communicating system architecture

5 Organizational Context

5.1 Key Relationships

• Manager, Analytics: Primary reporting relationship for performance management, professional development planning, and

organizational direction; provides overall accountability for delivery and career progression

• Lead, Analytics: Primary source of day-to-day technical direction, code review feedback, architectural guidance, and handson mentorship; the closest working relationship for engineering execution regardless of formal reporting line

• Lead, Analytics (peer workstream): Collaboration partner on cross-cutting technical decisions, shared components, and

joint knowledge-sharing activities

5 ORGANIZATIONAL CONTEXT

• Associate I & Associate II, Analytics: Peer collaboration on delivery; opportunity to provide informal guidance and code

review feedback consistent with the Senior Associate’s growing experience

• Product Management: Collaboration on translating business requirements into technical execution plans, surface insights

from analytical work, and demonstrate value of delivered solutions

• Operations Stakeholders: Engagement to validate solution effectiveness, understand real-world friction points, and ensure

delivered solutions meet operational realities

5.2 Active Workstreams for the Incoming Senior Associate

The incoming Senior Associate joins a team with active program momentum across several high-priority initiatives. Key areas of

contribution include:

• Frictionless Initiative: An active portfolio of AI/ML solutions targeting measurable reductions in revenue cycle friction; the

Senior Associate will own model development, pipeline execution, and delivery of defined initiative milestones

• Agentic AI Development: Active design and development of autonomous RCM agents targeting production deployment in

2026; the Senior Associate will contribute to agent implementation, testing, and integration under Lead direction

• Emerging Technology Adoption: Ongoing evaluation and integration of Microsoft Copilot, MCP, and Power Automate into

organizational workflows; the Senior Associate will prototype, build, and document adoption use cases

• Code Quality Uplift: An active program to elevate engineering standards across the team; the Senior Associate will participate as both a contributor and beneficiary—writing clean code, engaging in structured reviews, and applying feedback from

Lead-led workshops

Why Join US?

· Work on real-world healthcare and technology challenges , powered by emerging technologies and a strong innovation mindset.

· Be part of a fast-growing, people-first organization where your work creates measurable impact.

· Grow continuously with structured learning, certifications, and industry-recognized development programs .

· Collaborate with high‑caliber teams that value ownership, trust, and accountability .

· Grow alongside an organization that’s scaling with purpose and clarity. Be part of a fast‑scaling Global Capability Center with meaningful global responsibility.

Benefits:

· Comprehensive health insurance coverage for associate, kids (2) and parents supporting physical and financial well‑being beyond the workplace.

· Accidental insurance coverage for the associate that adds an extra layer of security.

· Professional development programs with reimbursement support to help you upskill and grow with confidence.

· A workplace that is fully compliant with labor laws , including maternity and paternity benefits.

· Thoughtful experiences like welcome kits, company swag, and work‑anniversary gifts that recognize your journey with us.

· Benefits designed to support you at different stages of life and career , not just on day one.

Senior Associate, Analytics(AI_ML)

About the role