Description

Drive enterprise-wide innovation by developing advanced machine learning and AI solutions focused on experimental design and causal inference.

Responsibilities

• Develop enterprise test-and-learn capabilities by investigating state-of-the-art experimentation practices and causal inference techniques.
• Design and execute data-driven experiments, including hypothesis creation, KPI selection, and multivariate testing.
• Build statistical and AI/ML models to analyze experimental data, assess significance, and derive actionable insights.
• Perform data wrangling, ETL, and data matching to prepare modeling datasets from diverse sources.
• Map and mine unstructured data, such as insurance contracts and medical records, to develop Deep Learning and Generative AI capabilities.

Required Skills

• 3+ years of hands-on experience in ML modeling and development.
• Strong programming proficiency in Python.
• Deep theoretical foundation in probability, statistics, and causal inference.
• Experience with PyTorch and/or TensorFlow.
• Expertise in designing, implementing, and deploying tests to assess consumer behavior.
• Solid background in algorithms and a wide range of ML models.
• Experience with Tableau and QlikView.
• Ability to work within Agile methodologies.
• PhD with 2+ years of professional experience, or a Master's degree with 4+ years of professional experience.

Key Skills

Education

Any Graduate