benshi.ai

Data Scientist - Experimentation and Causal Inference

We are looking for causal inference and experimental design expert data scientists. We are interested in individuals with a solid statistical or machine learning background, and experience with experimental design and/or causal inference techniques. You will be formulating and testing causal hypotheses using available data, and defining the best course of action to empirically assess the success or failure of planned actions.

Job detail

If you are passionate about using data science to evaluate the impact of real life interventions, and are excited about using your skills to improve the world, we want to meet you!

Responsibilities

  • Design, implement and validate experiments to assess the impact of actions taken by our partners, both app-based and in real life
  • Working closely with the engineering team in the definition, implementation, evaluation and deployment of causal algorithms and experimental features of our platform
  • Working closely with other data scientists and ML engineers is using available data sets to verify causal hypotheses

Minimum qualifications

  • BSc/BEng degree in mathematics, statistics, physics, computer science, machine learning or related fields; or equivalent technical proficiency
  • Experience in experimental design and/or causal inference
  • Solid coding skills in Python or R

Preferred qualifications

  • MSc/MEng or PhD in mathematics, statistics, physics, computer science, data science, machine learning or related fields; or equivalent research experience
  • Knowledge of advanced statistical techniques for A/B testing, bayesian experimental design, quasi-experiments, statistical matching techniques, observational causal inference and/or structural equation models
  • Knowledge of causal inference machine learning and sensitivity analysis
  • Experience in clinical trial assessment and/or causal inference for healthcare
  • Experience with causal inference libraries (doWhy, causalinference, causalml, causalnex...)
  • Experience in deep learning and its frameworks (TensorFlow, Keras, Caffe, Theano...)
  • Experience in time series forecasting, survival analysis, ensemble learning, recommendation systems and/or natural language processing
  • Experience with software development tools, source control, issue tracking, and continuous integration
  • Experience with machine learning lifecycle management tools (e.g. mlflow)
  • Experience with databricks