19 of 19 Permanent Sentiment Analysis Jobs

Amazon Connect flows for call deflection, self-service transactions, and escalation. o Enable and configure Contact Lens for real-time and post-contact analytics, transcription, summarization, sentiment analysis, and redaction. o Configure Amazon Q in Connect domains, knowledge sources, guided workflows, and step-by-step agent assist experiences. o Implement S3-based call and chat transcript storage … Preferred Qualifications • Practical exposure to full Amazon Connect deployments, including telephony setup, contact attributes, and queue performance optimization. • Experience enabling and tuning Contact Lens for compliance, sentiment analysis, and post-contact QA scoring. • Experience with Amazon Q in Connect for agent assist workflows and knowledge retrieval. • Experience with Amazon Bedrock (LLM and embeddings) and guardrails; calling LLM More ❯

Skills and Experience Required Strong capabilities across a range of AI techniques, including supervised and unsupervised learning, generative AI, deep learning, graph analytics, statistical modelling, time-series forecasting, geospatial analysis, NLP, sentiment analysis, and pattern detection. Excellent communication skills, with the ability to present complex technical concepts clearly and confidently to non-technical audiences, manage stakeholder More ❯

Skills and Experience Required Strong capabilities across a range of AI techniques, including supervised and unsupervised learning, generative AI, deep learning, graph analytics, statistical modelling, time-series forecasting, geospatial analysis, NLP, sentiment analysis, and pattern detection. Excellent communication skills, with the ability to present complex technical concepts clearly and confidently to non-technical audiences, manage stakeholder More ❯

recommendations, and ensuring data science practices align with organisational goals. Key Responsibilities Customer Insights: Build churn prediction models, apply clustering and segmentation, and use NLP for sentiment analysis to improve service quality and retention strategies. Operational Performance: Develop forecasting models for network demand, implement anomaly detection for outages and latency, and create predictive maintenance solutions to reduce … downtime. Efficiency & Planning: Optimise field operations through predictive scheduling and route planning, support inventory forecasting, and identify fraudulent or abusive usage patterns using ML techniques. Financial & Strategic Analysis: Deliver cost-to-serve models, profitability analysis, and scenario modelling to guide investment and regulatory decisions. Geospatial Analytics: Apply spatial modelling and visualisation techniques to support network planning, operational assessments More ❯

as Git. Required skills Experience in retraining LLMs on various datasets Conversational AI Python Docker Preferred Skills: Experience with specialized AI domains such as conversational AI, sentiment analysis, or recommendation systems. Knowledge of model interpretability techniques and responsible AI practices. Familiarity with MLOps pipelines for continuous integration and deployment of AI models. Experience with API development and More ❯

optimize key areas: Supply Chain & Logistics: Predictive analytics for demand forecasting, inventory management, and transportation optimization. Customer Engagement & Marketing: Personalized recommendations, AI driven customer insights, agent deployment, and sentiment analysis. Product Development: Advanced data analytics to improve footwear/apparel and innovation. Ensure AI models are explainable, scalable, and integrated with existing workflows. Communicate AI strategies, initiatives, and More ❯

in use across multiple products Developing new models focused on pricing optimisation, lead forecasting, and customer behaviour Contributing to new AI-driven initiatives, including text and sentiment analysis Collaborating closely with Data Engineering and BI to deliver robust, end-to-end ML pipelines and model monitoring Tech Environment: Python (pandas, scikit-learn, modern ML frameworks) SQL for … analysis and model input preparation Cloud environment (AWS/GCP/Azure) Exposure to NLP, MLOps, or CI/CD pipelines is a plus What We’re Looking For: Experienced Senior Data Scientist with a proven applied ML background Confident in owning end-to-end model development and optimisation Naturally collaborative, but comfortable working independently when needed Curious, pragmatic More ❯

templates using LLM summarization, structured extraction, and embedding-based clustering Craft intelligent prompts: Build and maintain the library of system prompts for ticket summarization, theme extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design … reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools: Experience with modern analytics platforms (Looker, Tableau, Mode, Metabase, etc.) Unique Strengths Simplification superpower: Exceptional ability to … Startup mindset: Self-starter who moves fast, wears multiple hats, and ships iteratively Bonus Points Experience with text analytics, NLP, or customer experience (CX) analytics Background in time-series analysis, anomaly detection, or forecasting Prior work at analytics agencies or consulting delivering bespoke reporting Exposure to AI infrastructure (vector databases, embeddings, semantic search) Experience at early-stage startups or More ❯

templates using LLM summarization, structured extraction, and embedding-based clustering Craft intelligent prompts: Build and maintain the library of system prompts for ticket summarization, theme extraction, sentiment analysis, and qualitative insights Define reporting standards: Set quality bars for what makes a great dashboard (agent performance, app store trends, CSAT drivers, topic evolution, etc.) Optimize RAG pipelines: Design … reports or products using LLMs—prompt engineering, structured output generation, embeddings, RAG, summarization pipelines Python proficiency: Comfortable with pandas, OpenAI library, API integrations, and data manipulation for prototyping and analysis SQL fluency: Can write complex queries and understand data modeling Analytics tools: Experience with modern analytics platforms (Looker, Tableau, Mode, Metabase, etc.) Unique Strengths Simplification superpower: Exceptional ability to … Startup mindset: Self-starter who moves fast, wears multiple hats, and ships iteratively Bonus Points Experience with text analytics, NLP, or customer experience (CX) analytics Background in time-series analysis, anomaly detection, or forecasting Prior work at analytics agencies or consulting delivering bespoke reporting Exposure to AI infrastructure (vector databases, embeddings, semantic search) Experience at early-stage startups or More ❯

or more general purpose programming languages including but not limited to: Python, R, Scala, Spark Experience with one or more of the following: Natural Language Processing, sentiment analysis, classification, pattern recognition. Development experience with AI frameworks such as TensorFlow, Microsoft CNTK, scikit, Keras, Caffe, Gluon, Torch. Familiarity with GenAI technology stack, including frameworks for prompt engineering, guardrails … junior team-mates to define and implement detailed measurement data, error logging/tracking for all products within our domain Initiate and lead code reviews and continuous improvement efforts Analysis and implementation of Performance/Stability/Reliabilit y initiatives Monitors metrics, problems, trends, and costs, in order to determine solutions, required process changes, etc. that will make the More ❯

use those insights to design more human, effective, and intelligent experiences. What you'll do: Interrogate and Analyse AI and contact centre data - explore transcripts, intents, summaries, and sentiment to uncover patterns in customer and advisor behaviour. Assess AI performance - review generative AI outputs, identifying issues around accuracy, tone, or bias, and recommend improvements into design, training, and … Visualise findings - create dashboards, heatmaps, and narrative reports that make insight easy to understand and act on and drive design direction. What you'll bring: Proven experience in " data analysis, customer research, or AI model evaluation "ideally within customer service, operations, or CX. Familiarity with " contact centre datasets "(transcripts, call categorisation, voice analytics, sentiment analysis etc. More ❯

Key Outcomes: Retain your customers, secure their renewal and hit retention targets Amplify Customer Loyalty: Champion customer retention, ensuring seamless renewals and surpassing retention targets. Master Sentiment Analysis: Identify and neutralise potential sentiment risks across diverse stakeholders, from end users to top decision-makers, fostering enduring relationships. Empower Customer Advocacy: Lead as a vocal customer More ❯

of novel NLP algorithms and models, pushing the boundaries of artificial intelligence. Design and implement advanced deep learning architectures for tasks such as text generation, summarization, sentiment analysis, and machine translation. Conduct cutting-edge research in areas like transformer models, large language models (LLMs), and explainable AI (XAI) for NLP. Collaborate with a global team of researchers More ❯

a pragmatic way Experience with analytical and quantitative problem solving using advanced statistical techniques, ML and generative AI methods for online content, e.g. sentiment from text, sequence analysis, forecasting and trend analysis Experience in building and deploying reproducible, production ready AI/ML models at scale, coupled with solid data engineering skills Prior knowledge of NLP More ❯

the current rule based, supervised and unsupervised methods Apply ML/DL algorithms and technologies to NLP tasks such as Named Entity Recognition, POS tagging, Parsing, Sentiment Analysis, Clustering, text prediction etc. Develop a comprehension of the technologies, methods and the architecture within Docusign product development. Also an understanding of the entire software development end to end More ❯

a software company based near Brighton. We design, develop and distribute our own products, including a Unified Communications App and AI-powered tools for call transcription, sentiment analysis and meeting summaries. We also build and support bespoke software applications for selected customers. Our team combines deep technical expertise with a passion for building scalable, cloud-deployed products More ❯

differentiated messaging that communicates how helps merchants build trust and grow through AI-powered review and customer insight tools. Translate new features and AI capabilities — from sentiment analysis to review response automation — into compelling value propositions for merchants and partners. Go-to-Market & Launch Execution Plan and execute GTM strategies for new features, integrations, and AI updates. … Partner with Product, Sales, and Growth teams to build unified launch plans that drive adoption and measurable impact. Customer & Market Insight: Conduct competitive analysis and customer research to uncover opportunities in the evolving eCommerce and AI landscape. Use insights to shape messaging frameworks, campaign strategy, and product feedback loops. Sales Enablement & Education: Create impactful content — sales decks, one-pagers More ❯

a pragmatic way Experience with analytical and quantitative problem solving using advanced statistical techniques, ML and generative AI methods for online content, e.g. sentiment from text, sequence analysis, forecasting and trend analysis Experience in building and deploying reproducible, production-ready AI/ML models at scale, coupled with solid data engineering skills Prior knowledge of NLP More ❯

surfacing, and context-aware automation. Collaborate with HCLTech Architects to define the chatbot strategy using Copilot Studio. Design AI for Supervisors including real-time dashboards, conversation intelligence, and sentiment analytics. Guide implementation of Intelligent IVR leveraging Azure Communication Services or integrated CCaaS platforms. Platform Integration & Governance Oversee the integration of telephony systems (e.g., Genesys, NICE, Five9, Azure Communication … integrations. Strong command of Copilot Service Workspace configuration and extensibility (e.g., session templates, macros, app profiles). Expertise in AI-powered features: agent assist, conversation intelligence, call transcription, sentiment scoring. Experience with Power Platform, including Power Automate, Power Apps, and Power Virtual Agents. Solid knowledge of Azure components such as Azure Communication Services, Bot Framework, Logic Apps, Cognitive More ❯