Role Description As a Data Mining Ideation Specialist, you will leverage your deep expertise in medical coding requirements, claims adjudication processes, and reimbursements methodologies to design and develop data mining audits that drive payment accuracy across our clients' Medicaid, Medicare, and Commercial lines of business. This role is ideal for someone who thrives at the intersection of coding, analytics, and audit development. - Translate complex reimbursement policies into actionable audit concepts that identify improper payments with a high degree of precision and consistency. - Apply strong analytical thinking, an in-depth understanding of claims data, and meticulous attention to detail to uncover overpayment opportunities and build scalable audit logic. - Define data selection parameters, validate outcomes, and continuously refine audit strategies to improve accuracy and impact. - Support production audit teams through training, guidance, and ongoing quality initiatives. This is a remote role. Salary Range 80-100K depending upon experience. Qualifications - 5+ years of complex claims processing and/or coding auditing experience in the health insurance industry. - Knowledge of all payer types including Medicare, Medicaid, and Commercial plans. - Prior experience in payment integrity audit development is highly preferred. - Mastery of CPT, HCPCS, and ICD coding standards. - Current Coding Certification: - AAPC Certified Professional Coder (CPC), Certified Outpatient Coder (COC) certification, or - Certified Coding Specialist (CCS) certification through AHIMA or - RHIT designation - Expert level understanding of medical claim coding and its impact on claim payments. - Ability to develop data mining audits, apply regulatory standards, and contractual requirements with credibility and objectivity. - Knowledge of legal, regulatory, and policy compliance issues related to medical coding and billing procedures and documentation. - Knowledge of health insurance operations, specifically with claims processing, billing, reimbursement, or provider contracting. - Prior experience in payer edit development, and/or reimbursement policy experience. - Expertise in data analysis and EXCEL. - Strong analytical and problem-solving abilities. - Effective communication skills. - Meticulous attention to detail. Requirements - Python or SQL coding skills. - Clinical background or experience. - Demonstrated experience translating technical jargon to non-technical end users. - Previous experience in the Payment Integrity space. - Exposure to EDI transactions (837, 835). - Experience using coding tools (such as 3M, Webstrat, and Encoder). - Collaborative mindset and ability to work effectively across cross-functional teams. - Proficiency in Outlook, Word, Excel, and other applications. - Ability to work independently and can multi-task or transition to different tasks easily. What Success Looks Like - Develop and deliver 6+ new audit concepts monthly, including logic specifications aligned with reimbursement policies. - Submit 4+ high-impact audit concepts per month, contributing to a minimum of $975K in identified overpayment opportunities. - Partner with internal teams to move concepts from ideation to client review and production implementation. - Ensure timely communication and follow-through: - Respond to inquiries within 1 business day. - Complete research and provide updates within 3 business days. - Drive resolution, including logic revisions or claim actions, within 5 business days. - Monitor audit performance, including yield and appeal outcomes, and continuously refine logic for accuracy and impact. - Collaborate with Operations and Management to support audit effectiveness and quality initiatives. - Complete "Second Look" reviews within established turnaround times.

• Develop, validate, and maintain MLOps pipelines, ensuring automation and integration with AWS services; • Design, implement, and evolve system architecture in a cloud environment; • Ensure the support, maintenance, and evolution of the workflow automation tool (n8n); • Collaborate with data scientists to operationalize and deploy Machine Learning models and LLMs; • Act on incident resolution and support tickets related to the MLOps environment; • Propose and develop technical solutions such as web applications, APIs, and agents based on LLMs; • Build and integrate APIs to support applications and data flows; • Monitor and optimize the performance of pipelines, applications, and production services.

• Serve as a coordination layer between sales and client-facing teams and the Audience Solutions data engineering team, ensuring segment requests are submitted with complete information and tracked through to delivery • Own intake documentation for incoming audience requests, gathering segment specifications, client deadlines, distribution targets, and data methodology requirements • Manage the AS project queue: track ticket status, surface blockers, and communicate timeline updates to commercial stakeholders • Support client-facing deliverables including segment availability confirmations, match test results, and audience performance summaries • Assist in maintaining the Audience Solutions segment catalog — keeping naming conventions, CPM tiers, and vertical classifications current and accurate • Support leadership on QBR prep, client reporting, and prioritization reviews • Serve as a first point of contact for inbound AS requests from internal sales teams

**👀 The Role** **Title:** Senior Data Engineer **Team:** Data **Location:** Remote UK **Salary bands:** £85,000 - £100,000 As Senior Data Engineer, you’ll help scale the data platform that powers decision making across Midnite. You’ll work on core platform improvements, reliable data pipelines, modern data tooling, and lower-latency analytics foundations that support the next stage of company growth. This is a high-ownership role for someone who brings strong engineering fundamentals and wants to shape how data engineering is done as the team scales. **You will:** - Design, build, and maintain scalable data pipelines using Python, SQL, Snowflake, Dagster, dbt, and AWS. - Own end to end data engineering projects from ingestion through to analytics enablement. - Develop and optimise reliable SQL based data models. - Help build a stronger staging and testing environment for data changes before production. - Improve monitoring, alerting, and data quality across key pipelines. - Contribute to CDC and lower latency data movement initiatives. - Work closely with Product, Analytics, Engineering, and business stakeholders. - Participate in code reviews and help raise engineering standards across the team. - Pair with other engineers where needed and support more junior team members as the team grows.