About the role
ComboCurve is a industry leading cloud-based software solution for A&D, reservoir management, and forecasting in the energy sector. Our platform empowers professionals to evaluate assets, optimize workflows, and manage reserves efficiently, all in one integrated environment.
By streamlining data integration and enhancing collaboration, we help operators, engineers, and financial teams make informed decisions faster. Trusted by top energy companies, ComboCurve delivers real-time analytics and exceptional user support, with a world-class customer experience team that responds to inquiries in under 5 minutes.
Oil and gas equities are having a moment, and most of the research being written about them is modeled top-down off company guidance. We can do something almost nobody else can: build from the well up. Well-level forecasts and economics in ComboCurve, rolled into a corporate model, rolled into a view on the equity. Same engine, two products.
This role owns both halves:
Basin and play research. Top-wells rankings, subplay studies, completion-design trends, operator benchmarking. Audience is E&P executives.
Equity research. Coverage of public E&P names built on our own well-level forecasts rather than on management’s. NAV and inventory, corporate cash flow, leverage, hedge position, valuation. Audience is investors and the executives who care what investors think.
The through-line is that our conclusions are built from the rock upward, and we can show our work at every layer. When our NAV disagrees with a company’s own disclosure, we can point to the wells and say why. That is the whole product.
This is a technical role with a public face. The output is research, not marketing copy. But the audience is made up of decision-makers, and the writing and design have to earn their attention.
Every study gets published at two depths. The two-page executive summary is what circulates: the ranking or the call, the type curve, the headline numbers, the methodology in brief. Underneath it sits the full study, which is where the actual argument lives. Subplay-level type curves, completion trends by vintage, spacing and parent-child effects, inventory depth, corporate model output, sensitivities across price decks, and a methodology appendix complete enough that a skeptical engineer or analyst could rebuild your numbers. The summary earns the meeting. The full study survives it, because the person who asks the hardest question will ask for the long version.
You will write both. They are different skills and we need both in one person.
What you’ll do
Design and defend the study
Build well study populations from commercial and public data, then define screening criteria that make the population honest: vintage floors, minimum lateral length, completion-intensity thresholds, minimum production history, re-stimulation exclusions.
Publish the screening waterfall. Every study shows how the population went from unfiltered to final, and what each criterion removed.
Know where the data lies. Null proppant, perforated vs. drilled lateral length, re-fracs with two completion events booked under one EUR, mis-assigned operators after an acquisition.
Forecast and evaluate
Run decline curve analysis across hundreds to thousands of wells: Arps parameters, b factor, Di, terminal decline, forecast QC at scale.
Build type curves and P10/P50/P90 bands; normalize EUR to a common lateral length so rankings aren’t just a proxy for who drilled longest.
Run full-cycle economics: vintage-tiered and lateral-length-based CAPEX, fixed and variable OPEX, flat and strip pricing, NPV, IRR, and payout, with sensitivities when the conclusion depends on price.
Roll it up to the company
Build corporate models off the well-level work: production forecast by asset, capital program, cash flow, leverage, hedge book, NAV, and inventory depth at various price decks.
Reconcile against public disclosure. Read the 10-K, the reserve report, the investor deck, and the transcript, and be specific about where and why your numbers differ.
Form a view. Valuation framing, relative comparisons across the peer group, and what has to be true for the market to be right or wrong.
Map it
Spatial analysis and cartography in QGIS or ArcGIS: subplay delineation, EUR interpolation surfaces, permit and lateral overlays, acreage and inventory footprints by operator.
Tie results back to geology and geography (subplay, TVD, thickness, pressure regime) so the conclusion says something about the rock and not just the operator.
Publish at both depths
Write the full study: [20-50] pages of analysis, exhibits, and a methodology appendix that lets a skeptical reader reproduce the work.
Then cut it to two pages without losing the thing that made it defensible. Compression is the harder half of this job.
Own the layout, charts, footnotes, and caveats in both. Ambiguity in a footnote becomes a credibility problem three weeks later.
Hold a publishing cadence of [1-2 studies per month], plus responsive pieces when the market moves. Cadence is set by the full study, not the summary.
Keep the models and datasets organized enough that coverage gets refreshed rather than rebuilt.
Get it in front of people
Present at conferences and webinars (URTeC, SPE ATCE, NAPE, energy investor conferences), and support executive and investor conversations where the research opens the door.
Work with marketing on distribution and with the exec team on what to cover next.
Feed the product
Your workflow is the customer workflow. Where the platform slows you down, product should hear about it with specifics.
What we're looking for
Required
Technical foundation in subsurface: BS in petroleum engineering, geological engineering, or geoscience, or equivalent hands-on experience.
3+ years in energy research, technical evaluation, or energy equity research and investing.
Production forecasting fluency. You can explain what a b factor above 1.0 implies for a forecast and when you would override it.
Economics fluency: NPV, IRR, payout, price decks, CAPEX tiering, differentials. You can say what the answer is sensitive to.
Corporate modeling. You can get from a well-level forecast to cash flow, NAV, and a valuation view. You do not need to have run coverage on your own yet, but you need to have built the model, not just read the output.
Comfortable in public filings and reserve reports, and skeptical of them in the right places.
Hands-on with commercial well databases (S&P Global, Enverus, TGS, or similar) including their failure modes.
Long-form technical writing. You can hold a thesis across a long document without it turning into a data dump, and build a methodology appendix someone could audit.
Compression. You can take that same study down to two pages and lose none of what makes it defensible.
Work you can show us and claim. Published research, investor materials, papers, or internal studies that changed a decision. A team byline is fine. We want to hear which parts were yours.
Willingness to be the named author. You will be cited, quoted, and occasionally argued with in public, and we will put your name on it from the first piece.
Strongly preferred
ComboCurve experience, or deep familiarity with a comparable forecasting and economics platform (ARIES, PHDWin, Val Nav).
Sell-side, buy-side, or data and analytics research experience, and any audience that already reads you.
QGIS or ArcGIS at a production level.
Python or SQL for data prep, QC, and batch work; BigQuery or similar a plus.
Multi-basin exposure: Permian, Haynesville, Eagle Ford, Bakken, DJ, Appalachia, Uinta.
Visual and design judgment, or the tooling to execute it (Figma, Illustrator, or equivalent).
Originally posted on Himalayas