Guideline

• Articulate the value of how our data and the rich insights around the market can help solve a challenge or offer a solution the client might be facing. • The role will be primarily tasked with lead generating so prospecting new business will be a significant amount of time spent. • Build and manage a robust pipeline of inbound and self-sourced leads, nurturing relationships and advancing potential buyers through each stage with discipline and rigor. • Educate and guide prospects through the buyer’s journey, demonstrating how our insight specific solutions can fuel their business growth, identify white space and generally educate them about the market. • Close new business and existing customer upsell deals, consistently meeting or exceeding quota targets. • Work closely with marketing, technology, and sales development teams to execute the sales strategy, keeping pace with a fast cadence of product enhancements and new releases. • Build strong partnerships with sales development representatives to collectively develop pipeline opportunities. • Bring unique perspectives, strategies, and ideas that contribute to our culture and future vision.

• Drive the product roadmap for the contributor vertical and the ad intelligence products within scope — the analytics products and query agent that turn Guideline's spend data into intelligence agencies and media companies act on. • Own the contributor vertical as a product problem — understand what creates onboarding friction, what drives attrition, and what makes a contributor expand their relationship with Guideline. • Build to address all three. • Own the scalability of the contributor stack — the ingestion infrastructure and data quality gates that determine whether Guideline can grow the contributor pool without compounding the problems that come with it. • Set the data quality standard — define what clean contributor data looks like, build the mechanisms that catch problems before they reach downstream products, and hold that bar as a product commitment. • Make it dramatically easier to become a Guideline contributor — the portal experience, onboarding workflows, and partner processes should turn a drawn-out integration into a competitive advantage. • Work directly with the CPO on ad intelligence product strategy and contributor growth; bring the client and market insight that connects platform decisions to product outcomes. • Build and deploy AI agents and skills as a routine part of the job.

• Set the strategy and roadmap for the ALD suite — make the hard calls about what gets built, what gets deferred, and why. • Own the relationships with ALD's most important clients — hedge fund analysts, quant researchers, mutual fund PMs. Their workflows drive the roadmap; you're in the room to hear them. • Take new opportunities from concept to client-validated conviction — Engineering engages after the evidence is in, not before. • Hold the quality commitments that differentiate Guideline in the market — coverage consistency and forecast accuracy are product promises, and you're accountable for keeping them. • Define how ALD data reaches clients — through data cloud marketplaces, direct API, flat file, or integration — and own the experience and reliability of each channel. • Be the most informed voice in the building on the competitive landscape; know where Guideline wins, where it doesn't, and what product decisions change the calculus. • Work directly with the CPO on domain strategy; bring the ground-level client and market insight that makes those conversations worth having. • Build and deploy AI agents and skills as a routine part of the job.

• Articulate the value of both our media planning management and data plus insights solutions to agencies and brands, tailoring your message to each prospect’s specific needs. • Prospect into accounts that may or may not have prior experience with our offerings. Be creative and persistent in finding new paths to revenue. • Build and manage a robust pipeline of inbound and self-sourced leads, nurturing relationships and advancing potential buyers through each stage with discipline and rigor. • Educate and guide prospects through the buyer’s journey, demonstrating how our solutions can fuel their business growth. • Analyze prospects’ business goals and challenges to determine strategic fit and prioritize your time on the highest-impact opportunities. • Close new business and existing customer upsell deals, consistently meeting or exceeding quota targets. • Work closely with marketing, technology, and sales development teams to execute the sales strategy, keeping pace with a fast cadence of product enhancements and new releases. • Build strong partnerships with sales development representatives to collectively develop pipeline opportunities. • Bring unique perspectives, strategies, and ideas that contribute to our culture and future vision.

• Support our growth and investor outreach initiatives • Identify and research high-potential prospects within the investor community using LinkedIn, financial databases, and web research • Use ChatGPT, Claude, and Canva to refresh, draft, and iterate on marketing collateral • Perform "small data" tasks, including cleaning investor lists and running queries in Excel • Deep-dive into specific investor profiles to understand their mandates, past investments, and strategic fit • Support our Client Success Team to improve client retention through targeted outreach programs • Translate complex data and research into clean, professional PowerPoint decks

• Data Cleaning, Parsing & Standardization (Core Function) • Build Python (Pandas/Regex) workflows to extract structure from free-form Prisma text fields containing placements, publishers, formats, devices, tactics, etc. • Develop rule-based and pattern-based logic to recognize abbreviation variations, naming inconsistencies, and agency-specific formatting habits. • Create normalization scripts that standardize publisher names, platform types, channels, and other key taxonomies. • Maintain “classification dictionaries” and pattern libraries that improve accuracy over time. • Data Quality & Operational Support • Profile incoming agency data to identify common quality issues and propose concrete remediation. • Design QA checks to detect missing fields, malformed entries, inconsistent dimensional labels, and mismatched spend totals. • Partner closely with Data Engineering to embed your logic into scheduled pipelines. • Own recurring operational workflows that support data ingestion, aggregation, and quality monitoring. • Dimensional Enrichment, Modeling & Data Derivation • Build and maintain predictive models to support media spend forecasting, pricing analysis, and text classification for data tagging and enrichment. • Apply statistical modeling or regression techniques to impute missing values or infer incomplete spend or performance metrics. • Derive new fields—such as format groupings, platform classifications, creative types, and targeting attributes—using pattern recognition and model-based inference across text fields. • Produce structured dimensions from unstructured data to improve downstream reporting. • Document the logic and assumptions behind model-driven derivations to ensure transparency and reproducibility. • Analysis & Internal Stakeholder Support • Investigate anomalies, unexpected trends, and data gaps by tracing issues back to source fields. • Build dashboards that help internal teams observe data quality, volume, deviations, and model-driven insights. • Provide ad-hoc deep dives into digital spend patterns, forecast accuracy, and classification performance for product, ops, and leadership.

• Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production. • Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness. • Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams. • Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases. • Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence. • Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams. • Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals. • Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation. • Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline. • Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights. • Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance.

• Represent Guideline's data solutions to institutional investors, building awareness and credibility within the capital markets vertical • Proactively identify and cultivate high-value prospective accounts, maintaining a robust and well-qualified pipeline of new business opportunities • Manage complex, multi-stakeholder sales cycles with confidence, navigating intricate negotiations through to close • Build and maintain senior-level relationships at target accounts, establishing Guideline as a trusted long-term partner to drive new business • Work cross-functionally with product, marketing, and sales development teams, leveraging a strong understanding of market needs and the unique value Guideline’s data solutions bring to institutional investors • Deliver accurate forecasts and weekly pipeline updates, leveraging Salesforce and Gong to manage and enhance your business strategies