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.