Data Science Tech Lead: GenAI at MD Clarity

London, England, United Kingdom

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Customer ExperienceIndustries

Requirements

Startup hacker mindset: Self-start from zero, respect no silos, carry work from prototype to production
AI-native dev tools as daily drivers: Cursor, v0, Claude Code (or similar)
7–10 years building production ML/back-end systems; 2+ years leading while coding
Expert Python; strong back-end chops (e.g., FastAPI, gRPC, Postgres, pub/sub/streams)
Agents & RAG: Fluency with at least one agent framework (ADK preferred). Proven track record shipping AI agents and building RAG pipelines
LLM + DS depth: Prompting/tooling, retrieval design, LLM evals; hands-on with time-series analysis (forecasting, change-point, drift)
Cloud & ops: Basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost control
Communication: Explain results clearly, align stakeholders, and write crisp docs
Bonus: DevOps wizardry; GPU/accelerator experience
Bonus: Multimodal pipelines (text + voice + screenshots)
Bonus: Prior experience in contact center/CX analytics or novelty/anomaly systems
Bonus: Founder or founding engineer experience

Responsibilities

50% Build: Design & ship agentic AI for CX (real-time assistants that listen to calls/chats, retrieve from customer KBs, draft responses with human-in-the-loop controls)
Structured extraction: Schema-driven pipelines over unstructured text (and other modalities) using retrieval, tool-use, and robust LLM prompting
Hybrid anomaly detection: Blend classical time-series methods (e.g., decomposition, change-point, forecasting) with LLM-aware, contextful detectors for seasonality, spikes, step-changes, and drift
Novelty discovery: Embedding-based clustering and drift, topic surfacing, LLM summarization of emerging themes with deduplication and evidence links
Alerting & scoring: Severity/impact ranking, de-noising, suppression/cool-downs, routing, and feedback loops
25% Architect & scale: Own reliability, latency, and cost. Design online/offline eval harnesses, canaries, and SLAs; operate GPUs/accelerators where needed
Stand up and harden RAG pipelines (indexing, retrieval policies, grounding, guardrails) and agent frameworks
Take basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost tuning
Participate in on-call for your area and drive root-cause analysis with crisp follow-ups
15% Collaborate: Pair with back-end & front-end to wire extractors/detectors and agents into ticketing, voice, and analytics stacks (APIs, webhooks, real-time streams)
Partner with PMs/CX to evolve taxonomies, schemas, and guardrails; translate business problems into shipped ML features
10% Align & showcase: Gather requirements from CX and product leads, demo new capabilities to execs & customers, and document impact with precision/recall, alert quality, latency, and cost metrics

Skills

Key technologies and capabilities for this role

LLMsRAGAI AgentsAnomaly DetectionTime SeriesEmbeddingsClusteringPrompt EngineeringRetrievalTool UseGCPCI/CDIaCObservabilityGPU

Questions & Answers

Common questions about this position

What is the work arrangement or location policy for this role?

The position is hybrid.

What salary or compensation is offered for this position?

This information is not specified in the job description.

What skills and experience are required for this Data Science Tech Lead role?

Requires 7–10 years building production ML/back-end systems with 2+ years leading while coding, expert Python with strong back-end skills (FastAPI, gRPC, Postgres, pub/sub/streams), fluency in agents & RAG frameworks (ADK preferred), and LLM + DS depth including prompting/tooling. A startup hacker mindset and use of AI-native dev tools like Cursor, v0, Claude Code are essential.

What is the company culture like at MD Clarity?

This information is not specified in the job description.

What makes a great candidate for this role?

A startup hacker mindset where you self-start from zero, respect no silos, and carry work from prototype to production, combined with 7–10 years of production ML/back-end experience, leadership while coding, and expertise in AI agents, RAG, LLMs, and tools like Cursor or Claude Code.

MD Clarity

Healthcare revenue cycle management software provider

About MD Clarity

MD Clarity provides healthcare revenue cycle management (RCM) software as a service (SaaS) to healthcare organizations like hospitals and clinics. Their software enhances price transparency, automates benefit verification, and ensures accurate reimbursement, helping clients reduce operational costs and improve revenue collection. A standout feature, Clarity Flow, automates front-end workflows to streamline patient intake and billing processes. The company's goal is to empower healthcare organizations to achieve greater financial efficiency and improve patient satisfaction.

Seattle, WashingtonHeadquarters

2010Year Founded

M_AND_ACompany Stage

Enterprise Software, HealthcareIndustries

11-50Employees

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Company Match

401(k) Retirement Plan

Health Savings Account/Flexible Spending Account

Wellness Program

Remote Work Options

Hybrid Work Options

Risks

Competition from established RCM providers like Epic and Cerner is increasing.

Potential regulatory changes may require additional compliance efforts and updates.

Rising demand for cybersecurity measures could necessitate more investment in security.

Differentiation

MD Clarity specializes in healthcare revenue cycle management with a SaaS model.

Clarity Flow automates front-end workflows, reducing denials and improving patient intake.

MD Clarity's data analytics capture missed revenue and reduce bad debt by 20%.

Upsides

MD Clarity named High Performer in G2's Summer 2024 Grid Report.

Partnerships with athenahealth and ModMed enhance integration with EHR systems.

Growing demand for patient financial experience aligns with MD Clarity's Clarity Flow.

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