Module Lead

JOB DESCRIPTION

Senior Data Science & Machine Learning Engineer

About Mphasis Javelina

Mphasis Javelina is a core administration and US healthcare payer platform engineered to modernize back-office operations for healthcare payors and Third-Party Administrators (TPAs). The platform is built around four key capability pillars:

• Core Administration: Delivers all-inclusive benefits administration, streamlining workflows and supporting multi-location operations.
• Claims Processing & Adjudication: Enhances claim repricing accuracy, accelerates processing times, and increases auto-adjudication rates.
• Digital Transformation: Leverages AI, cognitive computing, and Mphasis's Front2Back™ (F2B) framework to assist payors in transitioning from traditional fee-for-service models to value-based care models.
• Scalability & Compliance: Helps healthcare organizations maintain robust record retention, meet evolving regulatory requirements, and adapt to future industry demands at lower operational costs.

Position Overview

We are seeking an accomplished Senior Data Science and Machine Learning Engineer to join our Healthcare Innovation and Learning System (HILS) team based in Bengaluru, Karnataka, India. In this role, you will design, build, and operationalize machine learning models that directly empower Mphasis Javelina and the broader Javelina ecosystem — enhancing its claims processing, adjudication, digital transformation, and compliance capabilities. This is a full-time, on-site position requiring a minimum of six years of relevant industry experience.

Position Details

• Industry: US Healthcare Payer
• Seniority Level: Senior (6–9 Years)
• Employment Type: Full-Time
• Work Arrangement: On-Site — 5 days a week
• Work Location: Bengaluru, Karnataka, India
• Work Address: Global Village SEZ, Mylasandra, Bengaluru
• Work Timings: 9:00 AM – 5:00 PM IST

Key Responsibilities

All machine learning models and data solutions developed in this role are intended to empower Mphasis Javelina and the solutions that are part of the Javelina ecosystem.

• Develop a thorough understanding of the Javelina data model and its associated ecosystem components to inform model design and data strategy.
• Design, develop, and manage ETL pipelines using DBT to transform Javelina data into structured transformation layers fit for machine learning consumption.
• Architect and maintain synthetic datasets aligned with a holistic Healthcare Payer data ecosystem for specific Javelina use cases.
• Curate, cleanse, and prepare datasets for ML model development and training, including synthetic data for supervised and reinforcement learning scenarios.
• Select appropriate algorithms and model architectures to address specific business problems within the Javelina platform and the broader healthcare payer domain.
• Establish, manage, and maintain a robust MLOps environment to facilitate model training, versioning, deployment, and monitoring in support of Javelina.
• Design and implement automated validation and testing frameworks to ensure model correctness, completeness, and reliability.
• Expose trained models via REST APIs to support proof-of-concept development, demonstrations, and product integrations within the Javelina ecosystem.
• Define, capture, and publish metrics pertaining to model quality, performance, and usage.
• Produce comprehensive technical documentation of models for consumption by Javelina product and development teams.
• Continuously improve model performance through supervised learning techniques and iterative feedback loops.
• Identify and develop frameworks for performing supervised and reinforcement learning at scale.
• Collaborate with Javelina product development and support teams to define data requirements for building targeted models.
• Mentor and educate team members on machine learning best practices and model development methodologies.
• Evaluate emerging tools, technologies, open-source frameworks, and commercial ML models relevant to the healthcare payer industry.
• Engage with hyperscalers, startups, and strategic partners to identify models that complement and augment the Javelina product roadmap.

Required Technical Skills

Data Engineering & Transformation

• DBT (Data Build Tool) — advanced proficiency in transformation pipeline development
• SQL — complex querying, optimization, and schema design
• PostgreSQL and MySQL — relational database design and management
• Neo4j and graph databases (is a plus)— graph data modeling and querying

Machine Learning & Data Science

• Python — Pandas, NumPy, Scikit-Learn, PyTorch
• TensorFlow — deep learning model development and deployment
• Hugging Face — large language model integration and fine-tuning
• Amazon SageMaker — cloud-based ML training and inference pipelines

Preferred

• Linux — command-line proficiency and shell scripting
• Docker — containerization for reproducible ML environments

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• 6–9 years of professional experience in data science and machine learning engineering roles.
• Proven track record of delivering end-to-end ML solutions — from data ingestion through model deployment and monitoring — within a product-led engineering environment.
• Strong communication skills with the ability to convey complex technical concepts to non-technical stakeholders.
• Experience collaborating with cross-functional teams including product management, engineering, and external technology partners.
• Ability and willingness to work on-site at our Bengaluru office five days a week, adhering to standard IST business hours (9:00 AM – 5:00 PM).
• Preferred but not required: Demonstrated experience within the US Healthcare Payer domain; familiarity with payer data models, claims processing, and regulatory context is strongly preferred.

This job description is intended to outline the general nature and level of work performed by employees within this role. It is not an exhaustive list of responsibilities, duties, or skills required.