Data Scientist

Role Description

We're looking for a Data Scientist to join Event Time Advancements, the team behind Assaia's flight event time and delay prediction capabilities – especially departure delay prediction and related operational forecasting problems.

This is an applied ML role with end-to-end ownership. You'll work across the full lifecycle:

• Building datasets and features from evolving event histories
• Training and evaluating models
• Helping keep them reliable in live use together with backend engineering

A big part of the role is working on dynamic prediction problems rather than one-row-per-case modelling. The same flight or operational event evolves over time through repeated updates, and the model needs to make useful predictions from a changing picture as new information arrives.

This makes the work especially interesting for someone who enjoys:

• Temporal modelling
• Sequential reasoning
• Building systems that stay useful across multiple decision points

You should be comfortable making judgment calls on data trust, validation design, and trade-offs, and defending those calls to both technical and non-technical stakeholders.

The team builds and owns its own tooling for dataset generation, training, and parts of real-time inference – so software engineering discipline (testing, review, deliberate design trade-offs) matters here as much as modelling skill.

You'll directly shape live prediction quality for flight-event timing and delay-related capabilities used by operational teams, help determine which data is usable as ETA expands to new airports and prediction tasks, and turn one-off experiments into repeatable workflows the team can build on.

Responsibilities

• Modeling:
  • Train, tune, and evaluate models for departure delay prediction and related forecasts (event timing, duration, reason, occurrence).
  • Own tasks end-to-end: frame ambiguous operational questions as measurable ML problems, deliver evidence-backed recommendations.
  • Apply feature-based and neural approaches, including sequential models for evolving event histories.
• Data & Evaluation:
  • Build datasets and features from flight status, turnarounds, weather, airport context, and operational detections.
  • Audit raw signals for reliability, timeliness, and usability.
  • Design leakage-free offline evaluation that reflects real product use and respects temporal structure.
• Production Ownership:
  • Support inference code, monitor live quality, investigate regressions, and improve models when performance drifts.
  • Keep offline training aligned with live inference features and configuration; validate that offline results hold up in runtime.
  • Benchmark model output against existing estimates or baselines and explain trade-offs.
• Infrastructure & Collaboration:
  • Build and maintain internal tooling for dataset generation, training pipelines, and inference infrastructure – treated as real infrastructure: tested, reviewed, and built with deliberate design trade-offs.
  • Collaborate with backend and other engineering teams on live-system integration.
  • Maintain reproducible project assets: configs, experiment code, notebooks, reports, monitoring outputs.

Qualifications

• Strong Python engineering skills – packaging (poetry/uv), testing, typing, and performance-aware code.
• Working SQL: comfortable with joins, aggregations, and filtering to extract data.
• Writing typed, tested, maintainable Python that others can pick up, extend, and trust once it's running live.
• Fluent in Git-based workflows, with good code-review judgment.
• Practical experience with the Python data stack: pandas or polars, numpy, pyarrow, tree-based models (CatBoost, XGBoost).
• Able to use Docker and Kubernetes as a user, for running, debugging, and validating ML workloads.
• Familiarity with agentic coding tools (e.g. Claude Code, Codex).

Requirements

• Strong judgement in feature engineering, validation design, metric selection, missingness handling, label reliability, and leakage prevention.
• Solid grounding in statistics and probability, sufficient to reason about modelling choices and evaluation results.
• Open to and capable of extending into sequential or neural approaches.
• Understands the pitfalls of time-based or event-based modelling.
• Strong verbal and written English skills (B2+ level or higher).
• Some direct exposure to models in production.
• Comfortable owning a scoped, ambiguous problem end-to-end.
• Clear communicator with both technical and non-technical stakeholders.

Benefits

• Participation in making important decisions, your ideas will be heard and implemented.
• Always remote work and a flexible schedule.
• Paid vacation, paid sick leaves.
• Paid relevant courses/online education.
• Great company culture based on honesty and mutual respect.
• Live team events.