Senior Data Scientist: Predictive Operations & Experimentation

Key Responsibilities

Predictive Modelling & Algorithms

• Design, build, and deploy predictive models for core operational challenges, starting with demand forecasting and predictive rebalancing at the station level.

• Integrate heterogeneous signals (historical trip data, weather, events, seasonality, urban patterns) into production-grade feature pipelines.

• Continuously evaluate and iterate on model performance using backtesting and live monitoring.

• Explore and prototype adjacent model opportunities: maintenance prediction, fleet sizing optimisation, dynamic pricing sensitivity.

Data Analysis & KPI Development

• Propose and define KPIs and operational metrics that reflect real business impact (e.g., rebalancing cost per trip, station availability rate, idle time reduction).

• Perform deep-dive correlation studies and exploratory analyses to surface actionable patterns in operational data.

• Formulate and stress-test hypotheses before committing resources to live experimentation.

• Build dashboards and automated reports that make model outputs and KPI movements legible to non-technical stakeholders.

Experimentation & Causal Inference

• Design statistically rigorous experimentation frameworks (A/B tests, switchback experiments, difference-in-differences) that can be deployed across live city operations.

• Define sample sizes, control groups, and success criteria before experiments launch.

• Analyse experiment results, quantify uplift with confidence intervals, and translate findings into clear go/no-go recommendations.

• Own the feedback loop: model → hypothesis → experiment → measurement → model refinement.

• Tackle the unique challenges of experimentation in networked, real-world systems: account for spatial spillover effects between nearby stations, design cluster-randomised or switchback experiments that minimise contamination, and handle interference where treating one unit (station, zone, city) affects outcomes at others.

• Develop strategies for isolating treatment effects when supply and demand are shared across a network (e.g., rebalancing one station changes availability at neighbouring stations).

• Apply design-of-experiments principles to constrained environments: limited number of cities/zones, high variance, non-independence of observations, and operational constraints on what can be randomised and when.

Requirements

Must-Have

• 5+ years of hands-on experience building predictive/ML models on real-world operational or time-series data.

• Strong proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM) and SQL. Our data warehouse runs on Snowflake.

• Demonstrated experience designing and analysing controlled experiments (A/B tests, quasi-experimental methods).

• Solid grounding in statistics: hypothesis testing, regression, confidence intervals, power analysis.

• Ability to translate ambiguous business problems into quantifiable hypotheses and measurable outcomes.

• Clear communicator who can present model logic and experiment results to operations teams and leadership.

Nice-to-Have

• Experience with geospatial data, mobility/logistics, or marketplace-style supply-demand problems.

• Familiarity with causal inference techniques (uplift modelling, instrumental variables, synthetic control).

• Exposure to MLOps tooling (MLflow, Airflow, dbt, or similar) for model deployment and monitoring.

• Experience working with weather APIs, event data, or other external signal sources.

• Background in operations research or optimisation (e.g., vehicle routing, scheduling).

What We Offer

• Direct impact on how cities move. Your models run in production, not in notebooks.

• Ownership of the full data science lifecycle: exploration → modelling → experimentation → deployment.

• A small, senior team where your work shapes product and operational strategy.

• Flexible remote setup within Poland-compatible time zones.