Machinify, Inc.

Data Scientist

• Build and ship classical ML and GenAI pipelines that surface, rank, and explain candidate billing error, audit, and fraud concepts across large-scale healthcare claims data
• Advance state-of-the-art research in anomaly detection, information retrieval, and weak supervision, turning research into robust production systems
• Accelerate concept generation and concept validation with SMEs by designing human-in-the-loop workflows that enable high-throughput review with strong precision, recall, and traceability
• Own end-to-end delivery from problem framing and data strategy to model development, evaluation, deployment, and monitoring, with a focus on reliability and customer outcomes
• Partner cross-functionally with Product, Data Engineering, Operations, SMEs, and business leaders to prioritize work, unblock execution, and unlock material financial value
• Help scale the team and platform as Machinify grows aggressively toward resale and IPO readiness

Staff Data Scientist, Content Gen

• Solve real business problems with data: Own end-to-end data science work, from problem framing and data exploration to modeling, validation, and production impact.
• Lead high-impact initiatives: Drive complex efforts such as vendor leakage detection and prevention, delivering measurable revenue or cost improvements.
• Innovate on methods and approaches: Go beyond standard analyses by developing new metrics, models, or workflows when existing approaches fall short.
• Build and productionize models: Design, build, and support production ML or LLM-powered solutions in collaboration with engineering and product partners.
• Work deeply with data: Use SQL fluently to explore large datasets, build reliable data assets, and validate results.
• Influence cross-functionally: Partner with Product, Engineering, Finance, and Operations to align on goals, tradeoffs, and execution plans.
• Set technical direction (especially at L7): Raise the bar for data science quality, scalability, and impact across the organization through mentorship, design reviews, and technical leadership.

Staff Data Scientist – ML

• Translate medical policy into executable logic
• Build robust clinical feature pipelines
• Own measurement, evaluation, and continuous quality improvement
• Establish engineering frameworks and tooling
• Partner deeply with domain experts

Data Scientist – Clinical AI

Data Engineer

• Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets (CSV, Parquet, JSON).
• Lead efforts to canonicalize raw healthcare data (837 claims, EHR, partner data, flat files) into internal models.
• Own the full lifecycle of core pipelines — from file ingestion to validated, queryable datasets — ensuring high reliability and performance.
• Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with SMEs, Account Managers, and Product to ensure successful implementation and troubleshooting.
• Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability.
• Refactor and scale existing pipelines to meet growing data and business needs.
• Tune Spark jobs and optimize distributed processing performance.
• Implement schema enforcement and versioning aligned with internal data standards.
• Collaborate deeply with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, SMEs, and AMs to ensure pipelines meet evolving business needs.
• Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues.
• Contribute to the evolution of our data platform — driving toward mature patterns in observability, testing, and automation.
• Build and enhance streaming pipelines (Kafka, SQS, or similar) where needed to support near-real-time data needs.
• Help develop and champion internal best practices around pipeline development and data modeling.

Senior Data Engineer – Analytics

Product Operations

• Build a scalable, repeatable system that transforms strategy into shipped value
• Design and orchestrate the organization-wide Product Operating System
• Craft seamless interfaces between Engineering/Data Science/Operations/Product Management and Sales/GTM
• Establish a governance framework with meaningful gates
• Build frameworks for measurement, learning, and experimentation
• Lead initiatives that elevate AI adoption
• Implement capacity planning and portfolio management systems
• Coach PMs and partners on templates and decision hygiene

Staff Data Scientist, Content Gen

Staff Data Scientist – ML

Data Scientist – Clinical AI

Senior Data Engineer – Analytics

Product Operations Manager