Valtech

SEO Marketing

• Own organic content performance
• Develop and execute a global SEO content strategy aligned to Valtech's positioning as The Experience Innovation Company
• Conduct keyword research and competitive analysis to identify content gaps and topic clusters across key verticals — retail, automotive, financial services, health and B2B
• Partner with regional marketing teams to ensure global strategy translates into locally relevant execution
• Create and commission content that ranks and resonates
• Brief and develop long-form content — thought leadership, guides, case study landing pages and pillar pages — with clear direction on search intent, structure and narrative angle
• Work with subject matter experts, practice leads and leadership to surface Valtech's most distinctive thinking
• Guard Valtech's brand voice across all content you commission: confident, optimistic and human — never jargon-heavy
• Drive continuous improvement
• Track and report on organic traffic, keyword rankings, content engagement and conversion performance
• Conduct regular content audits and prioritise a refresh backlog that maximises SEO impact
• Stay ahead of algorithm changes, AI-driven search trends and LLM-optimised content (LLMO) best practices
• Collaborate across the marketing ecosystem
• Embed SEO thinking into the content creation process alongside copywriters, designers and editors
• Work with the Martech, Demand Generation and Campaign teams to support the full funnel
• Partner with the web team to implement on-page SEO best practices — metadata, internal linking, structured data and page experience signals

Senior SEO Content Strategist

AI Engineer

• Design and build agentic AI systems that facilitate conversational workflows for seller enablement
• Develop a chat-based seller assistant that:
• Answers seller questions using enterprise data
• Provides insights on ordering, pricing, and customer behavior
• Generates pre-visit one-pagers to guide sales conversations
• Integrate large language models (e.g., Claude, ChatGPT) with internal data sources and APIs
• Apply prompt engineering, context engineering, and agent orchestration techniques to improve reliability and usefulness
• Build and evolve the platform using LangChain and LangGraph (initially in Python)
• Design persistence and data access layers using Mongo or other data stores
• Collaborate closely with product, analytics, and engineering partners to shape both technical direction and use cases
• Serve as a champion for modern AI coding assistant tools (e.g., Cursor) within the team
• Help define standards and best practices as this AI capability scales

Consultant

• Analyze media audiences, channels, target audiences, and placement locations;
• Interpret media briefings;
• Develop and present strategic and tactical media plans;
• Lead the operational process of requesting and evaluating proposals from media outlets;
• Implement digital campaigns, monitoring and optimizing their performance;
• Oversee and track published creative assets;
• Monitor the entire post-campaign process, preparing and presenting reports according to campaign, investment, and timeframe;
• Track potential media balances, contracts, and financial flows;
• Issue and send PIs/Purchase Orders to media outlets;
• Provide information to account management to support development of negotiation strategies with suppliers;
• Identify and monitor media and market trends, as well as keep track of competitors' media strategies;
• Develop mechanisms to track campaign performance and measure success against established business KPIs.

Senior Digital Media Consultant

Data Scientist

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases
• Build production-grade systems that combine predictive, causal, and generative AI capabilities
• Turn complex data into actionable insights, automated decisions, and intelligent workflows
• Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI
• Collaborate closely with data engineers, ML engineers, analysts, and platform teams

Senior Data Scientist

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio.
• Work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights, automated decisions, and intelligent workflows.
• Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines.
• Collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture.
• Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems).
• Apply statistical, predictive, and prescriptive modeling techniques to solve business problems.
• Build reusable modeling frameworks that can scale across multiple domains.
• Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods).
• Translate observational and experimental data into actionable business insights.
• Embed causal reasoning into decision systems that guide actions (e.g., optimization, prioritization, trade-offs).
• Integrate GenAI capabilities (e.g., LLMs, RAG pipelines, agent-based systems) into data science workflows.
• Contribute to the development of intelligent agents and AI-assisted decision-making systems.
• Combine structured data models with unstructured data and GenAI outputs.

• Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within our AI portfolio
• Build production-grade systems that combine predictive, causal, and generative AI capabilities
• Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into real-time or near-real-time decision-making pipelines
• Collaborate closely with data engineers, ML engineers, analysts, and platform teams
• Develop and deploy machine learning models across use cases (forecasting, optimization, recommendation systems)
• Apply statistical, predictive, and prescriptive modeling techniques to solve business problems
• Build reusable modeling frameworks that can scale across multiple domains
• Design and implement causal inference methods (e.g., uplift modeling, experiments, quasi-experimental methods)
• Translate observational and experimental data into actionable business insights
• Embed causal reasoning into decision systems that guide actions

Data Engineer

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake)
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Design scalable logical and physical data models for analytical and operational use cases
• Apply modern architecture patterns including event-driven and streaming architectures
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence
• Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems

Senior Data Engineer

• Design and implement scalable data platforms and pipelines across cloud environments
• Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake
• Build ingestion, transformation, and curation workflows for both structured and unstructured data
• Implement modern data architectures including lakehouse patterns and medallion layering
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Enable data pipelines for GenAI use cases
• Design scalable logical and physical data models
• Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents
• Apply modern architecture patterns including event-driven and streaming architectures
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence
• Enable data serving layers to support downstream systems

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake)
• Developing reliable batch, streaming, and near-real-time pipelines
• Ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC)
• Establish strong data observability, including monitoring of data freshness and pipeline reliability
• Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions

Procurement

• Lead the end-to-end sourcing cycle for Professional Services and Consulting — from supplier identification and RFQ through to negotiation, contract execution, and renewal management.
• Build and manage a preferred supplier panel for consulting and professional services, establishing role-based rate cards and standard commercial terms across the category.
• Negotiate contract pricing, terms, and conditions directly with suppliers — optimizing for cost, quality, and risk mitigation in collaboration with Legal, Data Privacy, and Cybersecurity.
• Enable efficient and effective purchase services with a focus on business value, on-time delivery, and cycle time.
• Manage the portfolio of active agreements including scope, pricing, amendments, and renewals and coordinate intake requests for the category, delivering a seamless experience for internal stakeholders.
• Drive portfolio consolidation projects that reduce supplier fragmentation and deliver measurable cost savings, Identify and leverage value opportunities within the portfolio, including volume leverage, multi-year commitments, and cross-business unit consolidation.
• Oversee the sourcing cycle, handle tenders, and maintain strong relationships with suppliers, engaging effectively with multiple stakeholders.
• Benchmark supplier rates and market positioning against external references, providing credible commercial challenge to both suppliers and internal stakeholders.
• Maintain strong relationships with key internal stakeholders including delivery leaders, Finance, Legal, and senior management across NA & EMEA.
• Evaluate and continuously improve procurement processes within the category, contributing to playbook development and governance standards.
• Contribute to the broader procurement transformation roadmap, including system implementation, policy rollout, and AI-assisted tooling adoption.

Procurement Project Lead

• Design, build, and maintain reliable and scalable data pipelines, primarily leveraging SQL
• Develop and optimize complex queries to support analytics and reporting needs
• Ensure data accuracy, consistency, and quality across multiple data sources
• Partner with stakeholders to translate business questions into data solutions
• Support experimentation frameworks, including A/B testing and data validation
• Work with analytics teams to deliver datasets used in dashboards and reporting tools (e.g., Metabase or similar)
• Communicate insights effectively through data storytelling, tailoring messages for non-technical audiences
• Collaborate with cross-functional teams in a client-facing consulting environment
• Contribute to best practices in data modeling, documentation, and governance

Data Engineer – Mid-Level

Project Manager

• Oversee the delivery of brand marketing initiatives including analyst relations, industry awards, PR campaigns, and thought leadership programs.
• Partner with external PR agencies and internal stakeholders to coordinate media outreach and maintain relationships with journalists and analysts.
• Manage awards submission calendars, content creation, and cross-team inputs to ensure deadlines are met.
• Support the creation and dissemination of press releases, case studies, and brand storytelling assets.
• Define project scope, objectives, deliverables, and timelines in collaboration with stakeholders and the marketing teams.
• Develop and manage detailed project plans, schedules, and budgets for brand marketing campaigns.
• Track progress, manage risks, and resolve project roadblocks.
• Coordinate cross-functional input from design, content, digital, and product marketing teams.
• Keep global comms calendar up to date, working closely with Regional and Functional leaders.
• Serve as the primary point of contact for project updates across marketing leadership, PR agencies, and analyst firms.
• Manage expectations and alignment with internal and external stakeholders.
• Monitor and report on the impact of PR, awards, and analyst relations activities.
• Provide insights and recommendations to improve future brand initiatives.
• Identify opportunities for process improvements in planning and execution.

Senior SEO Content Strategist

AI Engineer

Senior Digital Media Consultant

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Procurement Project Lead

Data Engineer – Mid-Level

Data Engineer – Mid-Level

Marketing Project Manager, Brand and Comms