Data Science Tech Lead: GenAI

Data Science & GenAI Tech Lead — AI Agents, Structured Insights & Detection

Location: London, hybrid

Apply promptly! A high volume of applicants is expected for the role as detailed below, do not wait to send your CV.
About Anecdote

Anecdote we’re on a mission to make customer experience delightful for everyone involved . Think a real‐time copilot that listens to live calls and chats, reads our customers’ knowledge bases, and drafts high‐quality replies for human agents - while also turning messy, multi‐channel feedback into trustworthy structured insights, anomaly detection, and novelty discovery.

As the Tech Lead, you’ll own the technical vision and turn requirements into a live, reliable product used by brands like Grubhub, Booking.com, Dropbox, Uber, Careem, and Fubo. You’ll collaborate directly with engineers, other tech leads, directors, and the CTO to evolve ambitious prototypes into a rock‐solid, scalable platform.

What you’ll actually do

50% Build — design & ship

• Agentic AI for CX: Real‐time assistants that listen to calls/chats, retrieve from customer KBs, and 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.

What makes you a great fit

• Startup hacker mindset: You self‐start from zero, respect no silos, and carry work from prototype to production.
• AI‐native dev tools are your 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: You explain results clearly, align stakeholders, and write crisp docs.

Bonus points

• DevOps wizardry; GPU/accelerator experience.
• Multimodal pipelines (text + voice + screenshots).
• Prior experience in contact center/CX analytics or novelty/anomaly systems.
• Founder or founding engineer experience