About the Role We’re looking for an experienced Private Equity / Investment Banking professional to join our team as a Career Coach and help aspiring finance professionals break into competitive roles in private equity, investment banking, and related fields. This is a unique opportunity for someone who has real-world experience in finance and advanced financial modeling and also genuinely enjoys mentoring, teaching, and helping others succeed. You’ll work 1-on-1 with students through Zoom coaching sessions, helping them develop the technical skills, interview confidence, and career strategy needed to successfully recruit for highly competitive finance roles. What You’ll Do Conduct 1-on-1 coaching sessions with students pursuing careers in private equity, investment banking, and related finance roles Prepare students for technical and behavioral interviews, including mock interviews and detailed feedback Teach and coach students through complex financial modeling concepts, including: LBO modeling 3-statement modeling DCF analysis Transaction modeling Financial statement analysis Valuation Investment returns analysis Help students understand how to approach and communicate through PE and IB case studies and technical interviews Provide actionable feedback on students’ modeling skills, interview performance, communication, and overall recruiting strategy Help students develop compelling answers to behavioral and fit questions Coach students on how to effectively communicate their deal experience, investment theses, and financial analysis Share practical insights from your own experience working in private equity, investment banking, or related investment roles Help students understand what firms are looking for at different stages of the recruiting process Adapt coaching sessions to each student's background, experience level, and career goals Who We’re Looking For The ideal candidate combines genuine finance expertise with exceptional coaching and communication skills. You should have: Proven professional experience in private equity, investment banking, or a closely related investing/finance role Significant experience building and working with complex financial models, particularly LBO and transaction models A strong understanding of PE and IB recruiting processes and technical interviews The ability to clearly explain complex financial concepts to someone who may be learning them for the first time Strong interpersonal and communication skills A genuine interest in mentoring and developing students The ability to provide constructive, specific, and actionable feedback Strong interview skills and the ability to conduct realistic mock interviews The ability to tailor your coaching approach to different personalities, backgrounds, and skill levels Schedule & Flexibility This role is designed to be highly flexible. You will have the ability to set your own schedule and availability and book 1-on-1 Zoom sessions with students based on the times that work best for you. You can coach students around your existing professional or personal commitments without being tied to a traditional fixed schedule. What Makes a Great Coach We’re not simply looking for someone who is good at finance. The best coaches will be able to take a student who is struggling with an LBO model, technical interview, or behavioral question and break the problem down, explain it clearly, identify what they’re doing wrong, and help them improve. You should enjoy working with ambitious students and take pride in helping someone become more confident, technically stronger, and ultimately more successful in their recruiting process. Ideal Background Strong candidates may come from backgrounds such as: Private Equity Associate / Senior Associate / VP Investment Banking Analyst / Associate / VP Private Credit Growth Equity Investment Management Transaction Advisory / M&A Other highly analytical finance roles involving sophisticated financial modeling Prior coaching, teaching, mentoring, or interview preparation experience is a strong plus, but not necessarily required for candidates who have exceptional communication and people skills. Compensation Flexible, session-based compensation with the opportunity to earn based on the number of students and coaching sessions you take on. You control your availability and can choose how much you want to coach. Why Join Us? Set your own schedule Work remotely from anywhere Conduct 1-on-1 sessions via Zoom Help ambitious students break into highly competitive finance careers Leverage your PE/IB expertise outside of your traditional career Build meaningful relationships with the next generation of finance professionals Flexible workload with the ability to take on as many or as few students as you prefer If you’re an experienced PE or IB professional who loves finance and enjoys helping others learn, prepare, and succeed, we’d love to hear from you.

**About the work** We are a construction and construction management firm looking for a skilled data visualization freelancer to transform our preconstruction Excel deliverables — cost estimates, executive summaries, A3 documents, and historical cost tracking — into clean, branded, interactive HTML presentations our team can use in client meetings and sales presentations. We have already developed a working set of reference deliverables that define the quality bar, interaction style, and visual language we are looking for. Applicants will be asked to review these as part of the screening process. **What you will build** Each engagement typically involves one or more Excel workbooks that contain cost estimate data across multiple tabs. You will produce a standalone, browser-based HTML file that: - Organizes the data across 2–4 navigable slides or views - Applies our company brand colors, typography, and visual standards throughout - Includes interactive elements such as toggleable line items, live-updating totals, category filter presets, and exportable decision summaries - Can be opened directly in a browser with no login, no platform subscription, and no external dependencies beyond standard CDN-hosted libraries - Is clean and polished enough to present directly to a client or owner Deliverables are standalone HTML files — not Tableau dashboards, not Power BI reports, not Flourish embeds, not Python notebooks. If those are your primary tools, this project is likely not a fit. **Deliverable types we work with** - Executive summaries (cost phase comparisons, scope breakdowns, alternates calculators) - Two-page detailed cost estimates (line-item toggles, category presets, visible subtotals) - A3 concept summaries (project overview, qualifications, division-level cost detail) - Historical cost pathway documents (estimate movement across phases) **What we provide** - Source Excel workbooks with all relevant data - Robins & Morton brand standards including colors, typography, and logo guidelines - Reference HTML files showing existing deliverables at the quality and interaction level we expect - Clear direction on which tabs to prioritize and what information to surface vs. suppress in client-facing views **What we are looking for** - Strong proficiency in HTML, CSS, and JavaScript — you write clean, well-structured code directly, not through no-code tools - Experience with Chart.js, D3.js, or similar browser-native charting libraries - A design sensibility for data-dense, professional, client-facing presentations — not dashboards built for internal analysts - Ability to follow a brand system closely and apply it consistently across a multi-slide layout - Experience presenting financial or cost data to non-technical audiences is a strong plus - AEC, construction, or healthcare industry familiarity is helpful but not required

## Fractional Merchandise & Inventory Planner – DTC Apparel We are a fast-growing, 8-figure DTC apparel company looking for an experienced **Merchandise Planner / Inventory Planning Specialist** to help us build a better forecasting and replenishment system. This is **not a basic inventory-management or data-entry role**. We are looking for someone who has real experience forecasting inventory for a DTC/e-commerce apparel brand and can turn historical sales, inventory levels, marketing performance, and lead times into actionable purchasing decisions. ### The Problem We're Solving Our business historically operated around limited-edition product drops. As we've grown, we've identified a group of products that perform extremely well as evergreen products and through paid advertising. Our challenge is that we frequently: * Discover a winning product * Scale paid advertising * Sell through the inventory * Stock out * Lose the ability to advertise the product * Wait approximately 90–100 days for replenishment We want to build a system that allows us to **keep proven winners in stock without over-ordering unproven or declining products.** We have substantial historical sales and SKU-level data available. ### Initial Project We want you to analyze our historical product, inventory, and sales data and help us: * Identify products that should become evergreen/core SKUs * Analyze SKU-level sales velocity * Forecast future demand * Calculate weeks of supply * Establish reorder points * Recommend reorder quantities * Account for approximately 90–100 day production lead times * Establish appropriate safety-stock levels * Account for inventory already on purchase orders * Identify products at risk of stocking out * Identify products where we are overstocked * Incorporate seasonality where appropriate * Incorporate paid advertising performance into forecasts * Account for products whose demand can increase significantly when advertising spend increases * Analyze size-level demand and recommend size curves for apparel * Develop a practical weekly inventory/replenishment process our team can continue using ### An Important Part of Our Business We do **not** want every product treated as evergreen inventory. We operate a hybrid model: **Limited Drops:** Products intentionally produced in limited quantities where scarcity is part of the business model. **Core / Evergreen Products:** Proven products that can sell consistently and/or acquire new customers profitably through paid advertising. We want these products continuously available. A major part of this project is helping us determine **which products belong in each category and how aggressively we should replenish our proven winners.** ### Paid Advertising Matters This is particularly important. Historical sales velocity alone is not enough for our business. Some products may sell moderately through organic traffic but perform exceptionally well when advertised to new customers. These products can become major customer-acquisition drivers if sufficient inventory is available. We want forecasting to incorporate: * Organic sales velocity * Paid sales velocity * New-customer acquisition * Advertising scalability * Planned marketing spend * Product margins The goal is to prevent inventory availability from becoming the bottleneck that stops us from scaling profitable advertising. ### Deliverables The initial engagement should ultimately produce a usable forecasting/replenishment system showing metrics such as: * Current inventory * Units on purchase order * Historical sales velocity * Recent sales velocity * Forecasted demand * Weeks of supply * Lead-time demand * Safety stock * Reorder point * Recommended reorder quantity * Expected stockout date * Size-level recommendations * Overstock/understock flags We want something our team can review **weekly and actually use to make purchasing decisions**, not simply a one-time analysis or presentation. ### Ideal Background We're particularly interested in candidates who have: * Merchandise planning experience * Inventory planning experience * DTC/e-commerce experience * Apparel or fashion experience * SKU-level demand forecasting experience * Replenishment planning experience * Experience with long production lead times * Experience forecasting around promotions or paid advertising * Strong Excel/Google Sheets skills * Experience with Shopify data is a plus Experience working for or consulting with an **8-figure+ consumer brand** is strongly preferred. ### What We Are NOT Looking For We are not looking for: * A general virtual assistant * Basic inventory data entry * A generic data analyst with no merchandise-planning experience * Someone who only builds dashboards * Someone who requires us to develop the forecasting methodology for them We want someone who can look at our data and tell us: **"Here's what you should reorder, here's how much you should order, here's when you need to place the PO, and here's why."** ### Potential Long-Term Opportunity We would like to begin with a project analyzing our current inventory and building the forecasting/replenishment system. If the relationship is successful, we are open to an ongoing fractional engagement where you review inventory and demand with us weekly or monthly and continuously update purchasing recommendations. ### When Applying Please answer the following: 1. What DTC/e-commerce apparel or consumer brands have you done merchandise or inventory planning for, and approximately what revenue scale were they? 2. Describe a situation where a company was repeatedly stocking out of its best-selling products. How did you determine reorder points and quantities? 3. How would you forecast a product selling 20 units/day if paid advertising could potentially increase demand to 35–40 units/day and replenishment lead time is 100 days? 4. How would you determine which products should become continuously replenished evergreen SKUs versus remaining limited-edition products? 5. What data would you request from us during your first week? **Please begin your application with the words "MERCH PLANNING" so we know you read the entire posting.**

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.