• Understand the problems faced by organizations and link them to the right data and analytics solutions that support our clients in reaching their ambitions and meeting their challenges; • Assess and determine our clients’ organizational maturity in preparation of a data transformation; • guide organizations in this transformation towards a data-driven organization by collecting business, functional, and technical requirements, and preparing personas; • Visualize and incorporate your insights into clear presentations for stakeholders; • Use data storytelling to explain insights derived from the data to the client; • Design and implement data architectures on cloud platforms (AWS, Microsoft Azure, GCP) and build state-of-the-art data engineering pipelines for Analytics and (Generative) AI solutions; • Build, optimize, and test ML & (Gen) AI models; • Implement and scale solutions using CI/CD frameworks and Cloud automation (MLOps); • Prepare and facilitate meetings and presentations and lead client workshops; • Develop yourself and contribute to internal projects like creating innovative propositions, developing leading technologies, working on client proposals, and attending internal trainings and courses;

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements. • Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs. • Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services. • Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments. • Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals. • Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost. • Run A/B or uplift experiments to quantify impact and guide iteration. • Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

• Serve as the primary analytics leader across eCommerce, Marketing, Finance, Retail, Merchandising, Inventory Planning, and Operations. • Own problem definition for analytics - determining which business questions matter and what decisions insights should inform. • Move analysis beyond “what happened” to “why it happened,” “how it happened,” and “what we should do next.” • Proactively mine data to identify risks, opportunities, inefficiencies, and performance drivers. • Influence senior leaders through clear implications and actionable recommendations. • Own the integrity, governance, and usability of core commerce, customer, and operational datasets across platforms (eCommerce, POS, CDP, email, data warehouse). • Validate insights using business context and commercial logic — not just technical correctness. • Define, document, and continuously evolve company-wide KPI frameworks in partnership with Finance and functional leaders. • Identify and resolve cross-functional metric misalignment to ensure a single, trusted understanding of performance. • Establish standards for data quality, documentation, and analytical transparency across the organization. • Leverage AI as a core component of advanced analytical work, including hypothesis generation, exploratory analysis, pattern detection, insight synthesis, and narrative development. • Design, operationalize, and scale repeatable AI-assisted workflows that support recurring company-level decisions (growth, margin optimization, inventory productivity, forecast alignment). • Establish validation frameworks to pressure-test AI-generated outputs against source data, historical performance, customer behavior, and commercial logic. • Embed AI-enabled insights into planning, forecasting, and executive performance reviews. • Promote responsible, explainable, and outcome-driven AI usage across analytics workflows. • Own web and digital analytics strategy using Heap, including funnel performance, customer journey analysis, and conversion optimization. • Ensure tracking integrity and event schema accuracy, including review of structured data formats (e.g., JSON, data layers) in partnership with Product Engineering. • Translate behavioral data into commercial implications that influence merchandising, marketing, inventory, and operational decisions. • Ensure behavioral insights are aligned with downstream systems and financial realities. • Reconcile demand, margin, inventory, and operational perspectives into cohesive performance narratives. • Support planning, forecasting, and performance alignment across Finance, Retail, Merchandising, and Operations. • Challenge assumptions and reframe business narratives using data-backed insight. • Serve as a connector between commercial strategy and operational execution. • Establish analytical standards that prioritize clarity, trust, commercial relevance, and decision impact. • Reduce reactive ad hoc work by building scalable, decision-focused reporting and automation frameworks. • Design the structure, workflows, and expectations for the analytics function. • Hire, mentor, and develop junior analytics talent once foundational systems are established. • Champion a modern, insight-driven analytics culture across the company.

• Serve as the primary analytics leader across eCommerce, Marketing, Finance, Retail, Merchandising, Inventory Planning, and Operations. • Own problem definition for analytics - determining which business questions matter and what decisions insights should inform. • Move analysis beyond “what happened” to “why it happened,” “how it happened,” and “what we should do next.” • Proactively mine data to identify risks, opportunities, inefficiencies, and performance drivers. • Influence senior leaders through clear implications and actionable recommendations. • Own the integrity, governance, and usability of core commerce, customer, and operational datasets across platforms (eCommerce, POS, CDP, email, data warehouse). • Validate insights using business context and commercial logic — not just technical correctness. • Define, document, and continuously evolve company-wide KPI frameworks in partnership with Finance and functional leaders. • Identify and resolve cross-functional metric misalignment to ensure a single, trusted understanding of performance. • Establish standards for data quality, documentation, and analytical transparency across the organization. • Leverage AI as a core component of advanced analytical work, including hypothesis generation, exploratory analysis, pattern detection, insight synthesis, and narrative development. • Design, operationalize, and scale repeatable AI-assisted workflows that support recurring company-level decisions (growth, margin optimization, inventory productivity, forecast alignment). • Establish validation frameworks to pressure-test AI-generated outputs against source data, historical performance, customer behavior, and commercial logic. • Embed AI-enabled insights into planning, forecasting, and executive performance reviews. • Promote responsible, explainable, and outcome-driven AI usage across analytics workflows. • Own web and digital analytics strategy using Heap, including funnel performance, customer journey analysis, and conversion optimization. • Ensure tracking integrity and event schema accuracy, including review of structured data formats (e.g., JSON, data layers) in partnership with Product Engineering. • Translate behavioral data into commercial implications that influence merchandising, marketing, inventory, and operational decisions. • Ensure behavioral insights are aligned with downstream systems and financial realities. • Reconcile demand, margin, inventory, and operational perspectives into cohesive performance narratives. • Support planning, forecasting, and performance alignment across Finance, Retail, Merchandising, and Operations. • Challenge assumptions and reframe business narratives using data-backed insight. • Serve as a connector between commercial strategy and operational execution. • Establish analytical standards that prioritize clarity, trust, commercial relevance, and decision impact. • Reduce reactive ad hoc work by building scalable, decision-focused reporting and automation frameworks. • Design the structure, workflows, and expectations for the analytics function. • Hire, mentor, and develop junior analytics talent once foundational systems are established. • Champion a modern, insight-driven analytics culture across the company.