Junior Data Scientist

Uber Uber · Consumer · Krakow, Poland · Community Operations

Junior Data Scientist role focusing on foundational data science principles including Machine Learning, Experimentation, Statistical Modeling, and Causal Inference. Responsibilities include data collection, cleaning, analysis, predictive modeling, and communicating findings. The role involves translating business questions into testable hypotheses and experimental designs, or mathematical models and ML algorithms, with an emphasis on iterative delivery and best practices in code and analysis. Projects may involve Market Expansion, Market Health, Audits, and Document Processing using NLP/OCR.

What you'd actually do

  1. Formulate business questions into testable hypotheses and rigorous experimental designs
  2. Collect and clean data, analyze experiments, infer causality and estimate effects
  3. Use Statistical Modeling and ML to find patterns and make predictions from large datasets
  4. Engineer features, deploy models and improve them as needed
  5. Visualize and report your findings to Stakeholders, adapting your communication to a variety of audiences

Skills

Required

  • SQL
  • Python (Pandas Library, basics of OOP and API endpoints)
  • scikit-learn
  • scipy

Nice to have

  • causal inference
  • experimentation
  • econometrics statistics methods
  • statistical modeling
  • ML methods (neural networks, naive Bayes, SVM, decision forests, etc.)
  • feature engineering
  • model deployment
  • data visualization
  • stakeholder communication

What the JD emphasized

  • Knowledge of causal inference, experimentation and econometrics statistics methods for analytical problems
  • Ability to design, conduct and analyze experiments to infer causality and estimate effects
  • Understanding of statistical modeling and ML to find patterns and make predictions from large datasets
  • Skilled in SQL and Python (Pandas Library, basics of OOP and API endpoints)
  • Hands-on experience in common ML frameworks, tools & libraries (scikit-learn, scipy)
  • Knowledge of ML methods (neural networks, naive Bayes, SVM, decision forests, etc.)
  • Ability to engineer features, deploy models and improve them as needed

Other signals

  • Machine Learning
  • Experimentation
  • Statistical Modeling
  • Causal Inference
  • deploy models