Senior Data Scientist

Indio builds its own AI and large-scale data products for the energy industry. We don't take on client work: our data scientists and engineers build Indio's own products, work directly with the people who use them, and what we build goes to production. Parts of our work are confidential; we share more in interviews.

We're hiring a Senior Data Scientist to take on hard, open-ended modelling problems. You'll work with large, messy, real-world data and turn it into models that people rely on for decisions with real money behind them.

We keep the team small and the bar very high. We're looking for someone exceptional: the data scientist other data scientists go to when a model has to be right, and who can prove it.

Remote · 8 AM–4 PM US Central, Mon–Fri · Full-time · Competitive pay

That's 4 PM–12 AM in Romania.

The work

• Forecasting under uncertainty. Build models on sparse, noisy, irregular time series that hold up out of sample, come with calibrated uncertainty and stay stable as new data arrives.
• Making unstructured data usable. Turn large volumes of documents and semi-structured records into reliable, structured signals, using modern ML and LLMs where they help and simpler methods where they're enough.
• Agentic AI you can trust. Design multi-step LLM workflows that read, reason, check and hand off, and hold them to the same standard as any other model: measured, repeatable and explainable.
• Bringing imperfect sources together. Combine data from many sources of uneven quality, and measure and improve that quality instead of assuming it.
• Decisions, not just predictions. Quantify risk and value so the business can act on the full range of outcomes, not a single point estimate.
• Creating ground truth. Where no benchmark exists, work with domain experts to build the labelled data and evaluation that every model is judged against.

What you'll do

• Own modelling problems end to end: framing, data, method, evaluation and production.
• Agree measurable success criteria with the business and report honestly against them, including when a model isn't good enough yet.
• Design datasets, labelling guidelines and evaluation sets, and keep them current as the data changes.
• Build evaluation into development so every model, prompt or data change shows its effect before it ships.
• Design, evaluate and harden LLM and agentic pipelines: model selection, structured outputs, guardrails, cost and latency.
• Choose the right method for each problem, from statistics and classical ML to deep learning and LLMs, and back the choice with evidence.
• Write production-quality Python alongside our engineers, and keep your work reproducible, tested and documented.
• Explain results and uncertainty clearly to leadership, analysts and engineers.
• Help set the standards and the hiring bar for data science as the team grows.

What you've done

We hire on evidence, not titles or keyword lists. Every claim on your resume will be discussed in depth in a live, camera-on interview, so list only work you did yourself and can explain in detail.

• 6+ years in data science or applied ML, including several models you shipped that people relied on for real decisions.
• Measurable impact, with the numbers: an error rate you cut, a forecast you made more accurate out of sample, or manual work you automated.
• An evaluation you designed from scratch: the metric, the labelled data, the baseline, and the decision it changed.
• Forecasting or time-series modelling on messy real-world data, including backtesting and quantified uncertainty.
• LLMs or agentic systems in production, with quality you measured and improved, not just demoed.
• Algorithm design beyond library defaults: you've built or adapted a method when the off-the-shelf one wasn't good enough, and proved the new one was better.
• Breadth across tabular, time-series and text data, and the judgement to know which methods fit which.
• Strong statistics: you know when a result is noise, and you can explain why.
• Strong Python and SQL, clean tested code, and comfort working in a production codebase.
• Clear written communication in English. A redacted report, paper or design note is welcome.

Nice to have

• Experience in energy, oil and gas, or another asset-heavy industry.
• Geospatial data and spatial analysis.
• Bayesian methods, probabilistic forecasting or calibration.
• Graph databases or graph-based modelling.
• Work in regulated or high-stakes settings where every result must be explainable.
• An MS or PhD in a quantitative field, published research, or top results in competitions such as Kaggle, math olympiads or ICPC, or equivalent depth shown through your work.

How we work

Python, SQL and sound statistics are the core. Day to day you'll also work with frontier and open-weight LLMs, document AI, graph and geospatial data, and cloud data stores. We use AI coding assistants every day, and we expect you to use them well and check their work. The rest you can learn here.

Why join

• Real data at scale. Proprietary, real-world data with long history, not another clickstream.
• Hard problems. Open questions with no off-the-shelf answer, where your methods can set the standard.
• Direct impact. Your models feed decisions with clear financial outcomes, and you'll see the results.
• Experts beside you. Domain specialists who can tell you what's right and why.
• Ownership. A senior seat with a direct line to leadership and a real say in the roadmap.

Pay

Competitive salary, paid in USD or your preferred currency through Remote.com.

How we hire

1. Intro call (30 min, camera on): your background and what you want to work on next.
2. Deep dive (90 min): walk us through one model you shipped: the data, the metric, the baseline, the trade-offs, what broke and what you'd do differently. Expect follow-up questions several levels deep. Then we'll design a solution to one of our real problems together, without code.
3. Practical exercise (about 2–3 hours): a realistic dataset and an open question. We want to see how you frame it, evaluate it and explain it. AI tools are allowed.
4. Live review (45 min): you walk us through your exercise, and we change the question and work through it together.
5. Meet the team (45 min) with engineering, leadership and the domain experts you'll work with.
6. References from people who saw your work in production.

How to apply

Apply with your resume and, if you have one, a link to work we can inspect: GitHub, a paper, a technical write-up or a Kaggle profile. For each model you list, say what problem it solved, how you measured it, and what changed because of it. Resumes that list tools without results won't move forward. After the first review, we'll send a few short written questions, starting with the piece of work you're proudest of, and why. We also require a copy of any thesis or dissertation you wrote at university; we'll ask for it at the same time, and a redacted version is fine.