Analyze and perform Exploratory Data Analysis (EDA) on raw datasets with the ability to develop visualizations to present key findings from EDA, communicating with stakeholders to ensure understanding of potential opportunities, forming testable hypotheses, and obtaining regular feedback
Identify potential machine learning (ML) or Generative AI opportunities that drive business value and working them through our intake process to formally get placed on our backlog
Apply knowledge of statistics, machine learning, programming, and data modeling to recognize patterns, identify opportunities, test business hypotheses, and make valuable discoveries leading to operational savings or growth
Wrangle data to cleanse and prepare it for ML model development, including the engineering of features to improve model performance
Apply causal discovery and causal inference techniques to understand the relationships between variables and identify potential causal effects and causal relationships in datasets at scale
Manage and execute ML model life cycle management within our MLOps framework for all models developed
Collaborate with cross-functional and cross-organizational teams to integrate causal models into business processes and decision-making
Requirements & Skills:
Master’s degree in a quantitative field such as engineering, computer science, mathematics, statistics, or data science. Significant equivalent work experience in causal inference may be considered as a substitute
Strong understanding of and experience with causal discovery and causal inference methods
Experience in data science-related programming languages, such as Python, R, C, C++, SQL, etc
Experience with Big Data platforms, such as Spark, would be advantageous. Databricks experience is a bonus
Proven ability to apply advanced analytics techniques to real-world problems
Strong written and verbal communication skills
Ability to be effective in a team environment
Curiosity and enthusiasm to learn new domains
Experience with managing ML models through an MLOps lifecycle, from experimentation through production deployment to model version updates
Experience with distributed cloud providers such as Microsoft Azure, AWS, or GCP
PhD in a quantitative field such as engineering, computer science, mathematics, statistics, or data science; especially if the area of research is related to causal inference
Experience applying causal inference techniques in an oil, gas, and/or chemical manufacturing context
Experience or familiarity with working in an Agile team
Direct Experience using MLflow as an MLOps platform
