We are looking for an experienced U.S.-based digital marketing professional to manage and optimize our Facebook and Google Ads for a rapidly growing home services company in North Texas. We currently invest $25K per month in paid advertising and are looking for someone who is highly analytical, proactive, and focused on maximizing lead quality and ROAS—not someone who simply launches campaigns and checks in once a week. Responsibilities Manage and optimize Meta (Facebook/Instagram) and Google Ads campaigns Continuously monitor performance and make data-driven optimizations Improve ROAS, cost per lead, and lead quality Generate and test ad copy, creative concepts, and landing page ideas Analyze campaign data and identify opportunities for scaling Work closely with our team to understand our business and customer base Required Qualifications Extensive, demonstrable experience managing Facebook and Google Ads for US-based service businesses Expert-level experience with GoHighLevel (this is a requirement, not a preference) Strong understanding of CRM integrations, attribution, automations, and lead routing within GoHighLevel Experience with APIs, webhooks, or marketing system integrations is strongly preferred Excellent written and verbal communication in ENGLISH A Good Fit Will... Take ownership of campaign performance Bring new ideas rather than waiting for instructions Understand the numbers and make decisions based on data Be comfortable making frequent optimizations and testing new approaches Not a Good Fit If... Your agency primarily operates offshore. Your process is to "set and forget" campaigns with minimal ongoing optimization. You're charging premium retainers without delivering continuous testing, optimization, reporting, and strategic input. We're looking for a long-term marketing partner who wants to become an extension of our business and help us continue scaling across multiple markets.

About us 2POINT is a remote, full-service marketing agency based in San Diego, 20 years in. We're the CMO-level partners who stick around to execute: strategy, paid media, content, and more for clients who want a team that actually does the work, not just advises on it. Part of what we offer is AIObot, our AI-powered content engine, but the agency is the core. Leads are coming in the door across the board. We need someone whose entire job is to catch them fast and turn them into booked, qualified conversations. The role You own inbound. The second a lead comes in, you respond within minutes, run the follow-up sequence relentlessly, qualify them, and hand a warm, ready-to-talk conversation to one of our strategists. You are not the closer. You are the reason no lead ever goes cold. What you'll do • Respond to inbound leads within minutes during your coverage window • Run structured follow-up sequences in HubSpot across email, phone, and text • Qualify leads against our criteria before they reach a strategist • Book and confirm calls for our senior team • Log every touch and keep the pipeline accurate without being chased You're a fit if • You've done inbound sales, SDR, or appointment setting, ideally at or for a marketing agency • You understand how agencies sell and what business owners actually care about when hiring one • You can hold a credible conversation across the range of what we do, from strategy and paid media to content • You handle objections in real conversation rather than reading a script • You're comfortable talking speed-to-lead, connect rate, and set rate, because you've lived those numbers • You take coaching and adjust fast, since our messaging shifts as the market does • You're US-based with clear, natural phone and written English, a quiet space for calls, and reliable internet Bonus points • You get where AI fits into modern marketing services and can speak to a tool like AIObot credibly on a call • You've worked with AI and automation tools (n8n, Make, Zapier, GoHighLevel, and similar) • You're comfortable on video, since our strategists run Zoom calls and the handoff should feel seamless Details • US-based only • Available and responsive during core PT business hours • Part-time to start, around 30 hours a week, with a clear path to full-time as volume grows • Base plus a bonus per qualified handoff or per close To apply Start your proposal with the word SPEED so we know you actually read this. Then send a 30-second Loom or voice note answering one question: how would you follow up on a lead that showed interest and then went quiet? We want to hear how you think and how you sound before we hop on a call.

We build rank-and-rent micro sites: small, surgically targeted local service sites that own a city + service keyword set, capture inbound calls and form fills, and are then rented to a single local business on a monthly lead-flow agreement. We are not looking for a general "SEO guy." We are looking for someone who runs an AI-native SEO stack — DataForSEO as the data layer, Claude/LLM tooling as the reasoning and production layer, and automation (n8n / Make / GHL) as the ops layer — and who understands that in 2026 a micro site has to rank in three places at once: classic organic, the map pack, and inside AI answers (ChatGPT, Google AI Overviews, Perplexity, Claude). If your process is "install an SEO plugin and write 20 blog posts," this is not the role. The tactic, in detail 1. Market selection — pick a niche we can beat, not one we like Before a single page is built, the target market gets scored: Transactional keyword extraction. We target the words customers actually type ("EV charger installation cost"), not industry jargon ("level 2 charging infrastructure"). Volume and CPC pulled live from DataForSEO — no guessing, no "I think this gets searched." Competitor fundamentals head-to-head. Score the top 3 incumbents out of 100 across five categories: title tags/meta, heading structure, schema, page speed, image SEO. We only enter a market where we can win at least 4 of 5 on execution alone. Backlink weakness audit. Referring-domain count, per-domain authority, anchor distribution, spam signals. The green light looks like: a handful of rank-0 referring domains and anchor text that is almost entirely the bare brand name — a profile beatable with 6–8 genuinely good links, not volume. AI citation gap. Run an AI-visibility check on the incumbents. A competitor with zero citations across ChatGPT / AI Overviews / Perplexity is an open lane, and that lane is the single biggest reason this play still works. Deliverable: a go/no-go market brief with the scoring, the seed keyword set, projected lead volume, and the rent price the market can carry. 2. Build — a static asset engineered to load in under a second Static framework (Astro or equivalent), deployed to Cloudflare/Vercel, custom domain. Lighthouse 95+ across all four categories on localhost before deploy — not after, not "we'll fix it later." Full structured data: LocalBusiness / Service / FAQ schema, sameAs links to every real profile so AI engines resolve the site as an entity, not a URL. llms.txt published; robots.txt verified not to block GPTBot, ClaudeBot, PerplexityBot, or Google-Extended; key content confirmed server-rendered so AI crawlers can actually read it. Templates, not pages. One service-page template, one location-page template, one article template — then the city × service matrix is generated against them. 3. Content — every page exists to push the money page Money page first. One transactional page carries the primary intent. Its FAQ block answers the highest-value People-Also-Asked questions directly. Fan-out query targeting. Pull the sub-questions AI engines internally generate when answering the seed query, then write one thorough, source-cited piece per chosen fan-out query. This is the mechanism that gets the site cited rather than just indexed. Answer-capsule structure. TL;DR up top, cited live sources, clean heading hierarchy, schema on every piece. Internal link architecture with specified anchors. Supporting content builds topical authority; internal links route that authority to the money page. Done correctly, a 0-backlink page beats a 400-backlink page — we have seen it and we expect you to have too. No generic AI slop. Output must carry real operator-level specificity (pricing ranges, local detail, process steps, first-hand experience). We review for this and reject on it. 4. Local layer — map pack + NAP discipline Google Business Profile built and optimized: primary + secondary categories, services validated against actual search volume (zero volume = dropped), service areas, hours, photo program. Core citation set with a locked NAP block used byte-identical everywhere: Bing Places, Apple Business Connect, Yelp, Facebook, BBB, Yellow Pages, plus niche/regional directories matched to the vertical. Suspension hygiene is non-negotiable: no virtual offices, no mailbox addresses, no shared-address stacking, no cities the business does not serve. 5. AI visibility — the part most operators are still ignoring Baseline AI citation audit across ChatGPT, Google AI Overviews, Perplexity, and Claude, with a competitor scoreboard. Monthly re-check to prove the content work is actually moving citations, not just rankings. Reporting must show which queries the site now owns in AI answers, not a vanity score. 6. Instrumentation — a rentable asset is a measurable asset This is what converts a ranked site into a rentable one, and it is where most rank-and-rent builds fall apart: Call tracking number on the site and GBP, with recorded, timestamped call logs. GA4 key events for form fills, click-to-call, and bookings. Weekly rank tracking (organic + local pack) piped into a dashboard the renter can be shown. A CRO pass on the money page: offer clarity, above-fold click-to-call, short form, visible social proof. A lead we cannot count is a lead we cannot invoice for. Deliverables & milestones # Milestone Output 1 Market validation Scored go/no-go brief, keyword set, competitor weakness report, projected rent price 2 Build & technical Live site, Lighthouse report, schema validated in Rich Results Test + validator.schema.org, llms.txt, sitemap submitted in GSC 3 Content & structure Money page, city × service matrix, supporting fan-out content set, internal link map with anchors 4 Local & citations GBP live and optimized, citation set submitted with NAP audit sheet 5 Instrumentation & handoff Call tracking + GA4 events live, rank/citation dashboard, SOP doc so we can replicate the build 6 90-day performance window Monthly report: rankings, map-pack position, AI citations, calls, form fills What we expect you to bring Required Demonstrable rank-and-rent or local lead-gen sites you built and monetized — URLs, timelines, and lead volume. Case studies beat credentials here. Hands-on DataForSEO (API or MCP) or equivalent raw-data workflow. We want to see live data in the reports, not screenshots from a freemium tool. Working LLM-assisted production process — Claude/Claude Code, custom skills, MCP connectors, or your own equivalent. Tell us what your stack actually is. Schema, Core Web Vitals, and technical SEO you can defend line by line. Google Business Profile optimization and citation building, including suspension-risk judgment. Understanding of generative engine optimization: fan-out queries, citation tracking, llms.txt, crawler access, entity building. Nice to have Automation build experience (n8n, Make, GHL) for reporting and review workflows. Astro / static-site deployment on Cloudflare or Vercel. Multi-location or franchise SEO experience. Hard rules No PBNs, no spun content, no fake GBP listings, no address stacking, no Fiverr-tier link marketplaces. Anything that risks a suspension or manual action kills the asset and the engagement. No guaranteed-ranking promises. We evaluate on leading indicators and lead volume. Screening questions Link one rank-and-rent or local lead-gen site you built. What did it rank for, how long did it take, and what did it rent for? Walk us through how you would decide not to enter a market. What specific numbers would stop you? What is your data source for keyword volume and SERP data, and how does it get into your workflow? How do you get a page cited inside an AI answer, as distinct from ranking it in classic organic? How do you keep AI-assisted content from reading like AI-assisted content? Budget & terms Fixed-price by milestone for the pilot site, with a performance bonus tied to the 90-day lead volume. Propose your own number — include a rough hour split across the six milestones so we can see how you actually work. Strong pilot performance turns into a recurring build pipeline; we have a roster of markets queued. Please include the word "capsule" somewhere in your proposal so we know you read past the first paragraph.

We are looking for an experienced Meta Ads performance marketer to optimize an existing lead generation campaign....not someone to simply launch new ads. Our current stack: * Meta Facebook & Instagram Ads * GoHighLevel (GHL) CRM & Marketing Automation * High-intent audience data integrated into our campaigns * Lead generation for B2B services Your primary objective is to reduce our Cost Per Opportunity (CPO) while maintaining or improving lead quality. We're looking for someone who has a proven process for improving performance through: * Creative testing (UGC, static images, video, hooks, angles) * Offer optimization * Landing page and instant form optimization * Audience testing and segmentation * Campaign structure and budget optimization * Copywriting and messaging improvements * A/B testing methodology with data-driven decisions * Scaling winning campaigns This is not a project for someone who simply duplicates ads or changes targeting. We want someone who understands the entire acquisition funnel and knows how to systematically improve conversion rates and lower CPO. When applying, please include: 1. Examples of campaigns where you significantly reduced CPO or CPA. 2. Your testing framework (creative, offers, audiences, forms, etc.). 3. Industries you've generated leads for. 4. Your favorite Meta Ads optimization strategies that consistently move the needle. 5. Any experience with GoHighLevel and lead generation funnels. If you're someone who enjoys analyzing data, testing relentlessly, and finding profitable improvements, we'd love to talk.

We’re looking for a creative anchor for our paid media clients. You own ad creative from concept through final asset: design strategy, first drafts, revision cycles, and the production pipeline that gets variants out the door. This role requires using an AI-assisted production stack. You’ll spend your hours on the work that requires judgment — concepting, client-facing creative strategy, and revision decisions — not hand-resizing 40 banner variants. You'll work directly with our Head of Paid Media and ads managers. What you'll own - Creative strategy per client. You run the design strategy process for each account - competitor and market research, testing plans, and campaign concepts tied to performance data. - Static ad design. First drafts and full campaign suites for Meta, Google, and LinkedIn. Data-driven creative that's built to convert, not win awards. - Revision cycles. Internal and client feedback rounds, managed to a standard: tight turnarounds, limited rounds, clear rationale when you push back. - The AI production pipeline. You operate and improve our versioning/resizing workflow. When a new tool cuts production time without cutting quality, you test it and systematize it. - Video creative direction. Scripting, storyboards, and creative briefs for video ads. Editing execution is a plus. - Quality control. If it's not client-ready, it doesn't go out. - Asset hygiene. Working files, final files, and folder structure stay organized and findable. The next person can pick up any account cold. This role is paid ads production at scale — resizing, iterating, systematizing, and executing within clear brand guidelines. If you enjoy: - Clean systems - Clear feedback - Repeating what works (and improving it incrementally) - Using AI tools to move faster …this role will be a great fit. If you’re looking for expressive, experimental, or open-ended creative work, this is probably not the right seat. _________ Primary Responsibilities - Create and resize high volumes of paid ad creative across platforms: - Meta (Facebook / Instagram) - Google Display - LinkedIn (occasional) - Produce ad variations for: - Event stages (early bird → last chance) - A/B tests (copy, imagery, CTA emphasis) - Execute cleanly within existing brand systems - Incorporate feedback quickly with minimal revision cycles Occasionally support: - Simple motion graphics - Light video editing (short-form ads) - GIF creation Workflow Note A large portion of this role is production work. You’ll often be working from: - Existing templates - Prior winning creatives - Clear creative direction from our lead designer + media team - Speed, consistency, and accuracy matter more than originality Tools & Skills Required Must-Haves -Strong experience designing paid digital ads -Advanced proficiency in: -Adobe Photoshop -Adobe Premier Pro is a plus -Google Business Suite -Comfortable exporting for multiple platforms + specs -Strong attention to detail (typos, alignment, brand compliance) -Ability to follow systems and checklists -Great time tracking AI & Automation We strongly prefer designers who already use (or are eager to use): - AI-assisted image generation or enhancement - Background removal, upscaling, smart resizing - Template-based workflows - Any AI tools that reduce repetitive effort without sacrificing quality (You don’t need to use the same tools we do — just show us how you work faster.) Bonus - Scriptwriting & video editing skills - Experience inside an EOS, Traction, or ScalingUp company - Familiarity with Looker Studio, Google Analytics, or other reporting tools _________ You’re likely a strong fit if you: - Are detail-oriented and precise - Prefer clear direction over ambiguity - Enjoy executing and refining more than reinventing - Take pride in clean, error-free delivery - Can shift up or down in hours without friction - Are comfortable working with clients to talk creative strategy Tools you'll use daily - ClickUp, Basecamp, Slack, Zoom, Google Workspace, Claude, Zapier, Swydo, Vidyard

Bg: mission-driven education org that works in enterprises to teach software/agentic engineering under-the-hood so teams actually understand the tools and can own them rather than top-down exec-imposed ‘AI transformation’ We’re looking for a super-experienced engineering exec but who stays ‘on-the-ground’ and can partner w the CEO to help engineers in gov/enterprise to level-up their capacities (judgment, reasoning, system design etc) - while communicating/advocating to execs the value of empowered team members We’ll start w a 20hr project but opportunity for this to become long-term partnership - send over headlines on background etc (couple of sentences is gd)

Poker / Home Games Niche I'm looking for an organized freelancer to help me find the right creators, build a clean vetted list, and run first-touch outreach. What I need: - Research creators across Instagram, TikTok, and YouTube in the home poker, game night, and hobbyist poker world - Build an organized list: handle, platform, follower range, engagement, contact method, and a note on why they're a fit - Draft and send initial outreach once I've approved the list and the message - Keep responses tracked in a simple sheet You're a good fit if you've done creator sourcing or influencer outreach before, you're genuinely organized and you can write outreach that sounds like a person, not a mass blast. To apply: tell me how you'd go about finding hobbyist poker creators, and say I LOVE CREATORS in your message to me.

We’re a growing U.S.-based outsourcing company looking for a Growth Partner to help expand our presence across the U.S. real estate industry. We work with real estate investment firms, private equity firms, property management companies, developers, construction companies, family offices, startups, and growing SMBs by providing experienced professionals who become an extension of our clients’ in-house teams. Many businesses today face common challenges: * Rising labor costs * Employee turnover * Difficulty finding and retaining experienced talent * Limited internal bandwidth * The need to scale operations without significantly increasing overhead Our goal isn’t to replace employees—it’s to keep the engines running by providing dependable professionals who integrate into our clients’ existing systems, workflows, and teams. We can support clients with: * Property accounting, fund accounting, bookkeeping, AP/AR, bank reconciliations, month-end close, financial reporting, and reconciliations * Asset management support including rent roll analysis, T-12 reviews, budget vs. actual reporting, occupancy analysis, lease expiration tracking, CapEx tracking, KPI dashboards, investor reporting, and portfolio performance analysis * Financial analysis, underwriting support, Excel modeling, cash flow modeling, ARGUS-based financial modeling support, acquisition and investment analysis * Lease administration, CAM reconciliations, due diligence support, and operational reporting * Administrative support, executive assistants, virtual assistants, CRM management, research, marketing support, presentation preparation, and other back-office operations Every professional we place is thoroughly interviewed, reference-checked, and evaluated before being introduced to a client, ensuring quality, professionalism, and confidentiality. ⸻ Who We’re Looking For We’re looking for someone who already has relationships within the industry and regularly speaks with companies that could benefit from additional operational support. This could include: * Commercial real estate brokers * Recruiters * Property managers * Asset managers * Fund managers * Owners and operators * Consultants * Business development professionals * Anyone with strong relationships across the real estate, private equity, investment, or construction industries If you’re already speaking with firms that struggle with hiring, staffing, accounting, asset management support, or back-office operations, we’d love to connect. ⸻ Your Role * Introduce us to qualified opportunities * Build long-term relationships with decision-makers * Generate meetings and strategic introductions * Develop referral and partnership channels * Help shape our go-to-market strategy * Represent our business professionally within the industry ⸻ Why This Opportunity? We’re not looking for someone to simply generate leads—we’re looking for a long-term growth partner. If you’re entrepreneurial, well-connected, and believe in building long-term relationships, consider this more than a contract—consider it a business opportunity. As we continue expanding our client base, you’ll have the opportunity to grow alongside the business, contribute ideas, build strategic partnerships, and play a key role in our success. There will also be opportunities to represent the business at leading industry events such as ICSC, IMN, NMHC, and other real estate and investment conferences while helping expand our network across the U.S. We’re open to structuring the relationship through commission, retainer, project-based, or a hybrid compensation model, depending on experience and mutual fit. If this sounds like something you’d like to build together, we’d love to hear from you.

Enterprise SDR / Executive Appointment Setter (CFO & C-Suite Outreach) Hourly Rate: $35/hour + Performance Bonuses Hours: 15–20 hours per week Location: Remote (U.S. or Canada Preferred) About Us Compound Capital Connections is a rapidly growing consulting and technology company focused on delivering highly specialized financial recovery solutions to enterprise organizations. We combine proprietary technology, AI, and sophisticated outreach strategies to engage executive decision-makers at publicly traded companies. We are looking for an experienced Enterprise Sales Development Representative (SDR) who is comfortable engaging CFOs, Controllers, Tax Directors, General Counsel, Chief Supply Chain Officers, and other C-level executives. This is not a high-volume call center role. We provide targeted prospect lists, proven technology, and structured processes. Your mission is to open executive-level conversations and schedule qualified appointments. Responsibilities * Conduct outbound cold calls to executive decision-makers * Execute personalized email outreach campaigns * Perform LinkedIn prospecting and executive engagement * Leave professional, compelling voicemail messages * Manage opportunities within our GoHighLevel (GHL) CRM pipeline * Track all outreach activities and maintain accurate CRM records * Follow structured multi-touch outreach sequences * Qualify prospects and schedule appointments for senior leadership * Maintain a professional executive presence in every interaction We Provide * Highly targeted prospect lists * Company research and contact data * ZoomInfo intelligence * LinkedIn Sales Navigator * GoHighLevel CRM * AI-assisted outreach tools * Email templates and messaging * Defined sales processes and support * Ongoing coaching and collaboration Your focus is building relationships and securing executive meetings—not generating your own lead lists. Required Experience We are looking for someone with experience selling into mid-market or enterprise organizations. Preferred backgrounds include: * Enterprise B2B Sales Development * Executive Appointment Setting * Financial Services * Tax Consulting * Management Consulting * Enterprise Software (SaaS) * Supply Chain or Logistics * Customs Brokerage * Professional Services Experience engaging CFOs, Controllers, Tax Directors, or other executive leadership is highly preferred. Required Skills * Excellent spoken and written English (native or near-native fluency) * Strong executive communication skills * Comfortable making cold calls to senior executives * Professional email writing * LinkedIn outreach experience * CRM discipline (GoHighLevel experience is a plus) * Ability to manage multiple follow-up sequences * Organized, self-motivated, and results-oriented * Comfortable using AI tools such as ChatGPT or Claude to improve productivity Compensation * $35/hour * 15–20 hours per week * Performance bonuses for qualified appointments * Additional success bonuses tied to business outcomes * Opportunity for increased hours as results are demonstrated To Apply Please include the following: 1. A brief summary of your enterprise sales or executive appointment-setting experience. 2. The types of executives you have successfully engaged (CFOs, CEOs, Controllers, etc.). 3. The CRM platforms and sales tools you have used. 4. A short audio or video introduction (optional but encouraged). 5. Why you believe you would excel in this role. If you have a proven track record of opening doors with C-level executives and enjoy building meaningful business relationships, we’d like to hear from you.

Engagement: Contract, phased. Phase 1 is a paid, standalone feasibility engagement with a go/no-go gate before Phases 2 and 3 are commissioned. Duration: Phase 1 approx. 1–2 weeks. Full scope approx. 1–3 months. Your compensation and evaluation are tied to the rigor of the method, not to what the data shows. Wherever the evidence is insufficient to support a claim, documenting that is a required and fully compensated deliverable. Our product architecture already assumes selective output: we display a result only where the evidence supports one and return "insufficient evidence" everywhere else. Both halves of that map are load-bearing, and we have no commercial preference for how the cohorts divide between them. We are not looking for a trading signal, an alpha model, or a predictive engine, and work that drifts in that direction is outside the scope of this engagement. What we want is a rigorous, classical event study: given this category of news event and this type of stock, what is the historical distribution of abnormal returns, with honest confidence intervals, and does it hold out of sample? We would rather fund a well-specified analysis whose answer surprises us than a poorly specified one that confirms anything. About Us We are a news curation and context firm serving US equity markets. We deliver curated, context-enriched headlines to over 200 institutions and more than 3,000 institutional traders on both the buy and sell side. We have been doing this for decades and are among the fastest on the street. Two features of our data matter for this work: Our archive is curated, not scraped. Roughly 2,000 items per day pass an editorial filter applied by former traders, PMs, and analysts. The same editorial logic produced our historical archive and our live feed, so there is no train/live mismatch in the sample definition. Timestamps are reliable. We are frequently first to print. Delivery timestamps closely track information arrival. Every headline in the archive carries an AI-generated sentiment label, a directional strength score, an accompanying model confidence score, and classification into up to 25 taxonomies. These labels are inputs to your analysis. Assessing their stability over time is in scope; generating new ones is not. We are building a retail-facing product on top of this archive. Your work establishes its empirical foundation. The Core Question For a given cohort (defined by some combination of stock characteristics, news taxonomy, and sentiment tier) what is the historical distribution of abnormal returns over six horizons following a headline, and how much of that distribution is statistically distinguishable from the cohort's unconditional baseline? The six horizons: Horizon Window 1 ~60 minutes post-headline 2 ~120 minutes post-headline 3 End of day (4:00 PM ET close) 4 Overnight / session gap 5 3 trading days 6 5 trading days Horizon definitions must be measured in trading time, not calendar time, with defined handling for headlines crossing outside regular session hours. Scope of Work Phase 1 : Methodology and Feasibility (paid, standalone, go/no-go) Deliverable: a written methodology document sufficient for a second statistician to reproduce your approach, plus a feasibility assessment against a data sample we provide. Must address: Abnormal return specification. Definition of the market/sector model used to isolate idiosyncratic return. Raw returns and own-volatility normalization alone are not sufficient. Volatility baselines. Time-of-day-conditional volatility estimation for intraday horizons. A trailing daily sigma scaled to one hour will not do. Cohort structure. How to group and condition. Our 25 taxonomies were built for editorial routing, not statistical pooling; some likely merge, some likely split. Our team will work with you on this as we know the market behavior and expect to inform the groupings. Thin-cell handling. Ticker-level cohorts will often have very few observations while pooled cohorts have many thousands. We expect a partial-pooling / hierarchical or empirical-Bayes approach, or a reasoned argument for a different treatment. Minimum evidence threshold. Derive our display threshold as a function of a stated minimum detectable effect and target power. We want the reasoning, not just a number. Dependence structure. Treatment of overlapping estimation windows, multiple headlines on the same name in the same window, and cross-sectional correlation on macro days. Baseline construction. Every cohort needs its own unconditional baseline. Absolute hit rates are not meaningful on their own. Validation design. How you will establish that estimates hold out of sample. Phase 2 : Estimation and Validation Full historical estimation across all valid cohort × taxonomy × sentiment × horizon permutations. Walk-forward out-of-sample validation. Freeze the estimates on an early period, run forward on data you have not touched, and report how many qualifying cohorts held up and by how much the effect decayed. This is a required deliverable and a primary measure of the project's success. Multiple-comparison control appropriate to the number of cells tested. Deliverable: a static reference matrix (CSV or Parquet) containing every cohort, its point estimate, confidence bounds, sample size, unconditional baseline, and qualification status across all six horizons. Deliverable: an explicit register of cohorts where no reliable effect was found. Phase 3 : Logic Transfer Documented mathematical logic and a reference implementation (Python) sufficient for our engineering team to reproduce the calculations in production. The normalization scheme converting validated estimates into a bounded 0–100 display score. Our current thinking is that this should derive from the lower confidence bound on the effect over baseline, so that sample size enters through the statistics rather than as a hand-set weight — we welcome a better proposal. Working session with our engineering team and written sign-off. -Data and Tools- All data is provided by us. No data sourcing, licensing, or acquisition is required on your part. Historical curated headline archive with AI-generated sentiment, strength, confidence, and taxonomy labels. Historical pricing data , which we supply: minute and 5-minute OHLC bars for intraday windows, daily aggregates for multi-day windows, and participant timestamp data covering pre-market (4:00 AM – 9:30 AM ET) and post-market (4:00 PM – 8:00 PM ET) sessions. Extraction volume and format can be adapted to your requirements. Tell us what you need and how you want it delivered. Language and libraries are your choice. Python or R both fine. Methodological Constraints All methods must be classical, auditable, and explainable line by line. This is a retail-facing product; every number we display must be defensible to a regulator, a journalist, or a skeptical user. No black-box or machine-learning models in the estimation pipeline. No neural networks, gradient boosting, or ensemble learners. No LLM-assisted analysis or interpretation. To be unambiguous: standard econometric and statistical tooling is expected and welcome. Hierarchical and random-effects models, empirical Bayes, shrinkage and regularization, bootstrapping, and power analysis are all classical methods and all in scope. The constraint is on opacity, not on sophistication. Explicitly Out of Scope Production software development, database design, or streaming pipeline work. Any form of signal optimization, strategy backtesting, or portfolio construction. Generating or revising sentiment/taxonomy labels. Product, UI, or commercial strategy. Who We're Looking For Strong fit: Formal training in econometrics, statistics, or empirical finance. Direct experience with event-study methodology — abnormal returns, estimation windows, cross-sectional aggregation, the Brown & Warner / MacKinlay / Kothari & Warner literature. Published or applied work measuring the price impact of information events. Comfortable delivering a null result and defending it. Willing to tell us when part of our framing is wrong. Our team is made up of former buy-side and sell-side traders, PMs, and analysts and we know the market, we do not claim to be statisticians, and we want to be pushed back on. Poor fit: Quantitative researchers whose instinct is to iterate until something profitable appears. Anyone who would treat "no effect found" as a problem to be engineered around. Generalist data scientists whose primary toolkit is ML. Anyone who would execute the methodology above without questioning any of it. Academic researchers, finance PhD candidates and postdocs, and practicing econometricians consulting on the side are all encouraged to apply. Screening Task (Paid) We will shortlist a small number of applicants and pay each for a 2–4 hour written task at their stated rate before any larger commitment. The task: You have approximately 5,000 news events across approximately 800 US equities. Propose a methodology for estimating the probability of a directional 3-day abnormal return, conditional on event category and stock characteristics, where per-ticker event counts range from 3 to 400. Address: how you define abnormal return; how you handle thin cells; how you set a minimum evidence threshold for reporting a result; and how you validate that estimates hold out of sample. Two pages. No code required. We are evaluating reasoning and judgment, not polish or length. To Apply Please include: A brief description of the most relevant event study or abnormal-return analysis you have conducted, including what the data showed and what you concluded. One example of a project where your conclusion was that the effect was not there. What you did, and how you presented it to whoever commissioned the work. Your view on how to handle cohorts with very few observations, in two or three sentences. Any published work or writing samples. Availability and rate. Applications that restate this posting back to us will not be reviewed. We would rather see one paragraph of real disagreement with our approach than three pages of agreement. An NDA will be required before data access.