Data Scientist

Pango is looking for an experienced Data Scientist to join us and take a key role in turning large-scale, real-world data into smarter product and business decisions.With millions of users interacting with Pango's mobility services, our data reflects complex customer behavior, business models, and real-life mobility patterns.We're looking for someone who can go beyond building models — someone who knows how to identify the right problems, challenge assumptions, uncover opportunities in the data, and translate them into real business impact.

You'll have significant professional ownership, from taking an ambiguous business question and framing it as a statistical or Machine Learning problem, through choosing the right methodology, building and evaluating the solution, and bringing it to production.Just as importantly, we expect you to proactively identify questions that should be answered with data — even when nobody has asked them yet.

Requirements:

• 4–6 years of hands-on experience as a Data Scientist.
• Strong statistical foundation and hands-on experience with Machine Learning.
• Strong experience with Gradient Boosted Trees, such as XGBoost, LightGBM, or CatBoost.
• Hands-on experience with AI technologies, including Generative AI and foundation models, as part of real-world solutions.
• Strong Python and SQL skills.
• Experience developing and deploying models in a production environment.
• B.Sc. or M.Sc. in Statistics, Applied Mathematics, Physics, Electrical Engineering, Computer Science, or a related quantitative field.

Responsibilities:

• Own Data Science problems end-to-end — from business question and hypothesis to modeling, evaluation, and production.
• Partner closely with Product, Business, Data, and Engineering teams to translate complex business challenges into data-driven solutions.
• Proactively identify opportunities where statistical analysis, Machine Learning, and AI can improve our products, customer experience, and business performance.
• Build, train, evaluate, and improve Machine Learning models used in production, across both real-time and batch use cases.
• Select the right statistical or ML approach based on the problem and the data — including recognizing when a simple analytical solution is better than a complex model.
• Define meaningful evaluation methodologies and connect model performance to real business impact.
• Communicate complex statistical findings clearly to both technical and non-technical stakeholders.

Advantages:

• Experience with production ML platforms such as AWS SageMaker.
• Experience with real-time and batch inference.
• Experience with neural networks, sequence models, and embeddings.
• Experience working in a large-scale B2C or data-rich product environment.