6 of 6 Remote Time Series Analysis Jobs in the South East

Lead — AI Agents, Structured Insights & Detection Location: London, hybrid 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 … 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/ More ❯

Lead — AI Agents, Structured Insights & Detection Location: London, hybrid 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 … 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/ More ❯

Lead — AI Agents, Structured Insights & Detection Location: London, hybrid 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 … 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/ More ❯

basis. You will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems across domains such as computer vision, natural language processing, and time-series analysis. Key Responsibilities: Design and implement deep learning models using frameworks such as PyTorch or TensorFlow. Collaborate with data scientists, ML engineers, and product teams to define More ❯

basis. You will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems across domains such as computer vision, natural language processing, and time-series analysis. Key Responsibilities: Design and implement deep learning models using frameworks such as PyTorch or TensorFlow. Collaborate with data scientists, ML engineers, and product teams to define More ❯

basis. You will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems across domains such as computer vision, natural language processing, and time-series analysis. Key Responsibilities: Design and implement deep learning models using frameworks such as PyTorch or TensorFlow. Collaborate with data scientists, ML engineers, and product teams to define More ❯