Who We Are:

Bridgetown Research is a machine learning research lab that is building business analysis tools for consultants, professional managers, and ‘AI agents’.  Our goal is to help evaluate every management decision of material financial or strategic significance.

We are still in stealth-mode, but our core product is already being used by leading Due Diligence consultants (like McKinsey & Co., BCG, etc.) and private equity firms to evaluate investment decisions.  We are backed by leading investors from the US and India. 

Team:

Harsh Sahai (CEO) was an Engagement Manager at McKinsey & Co in Seattle. Prior to this, he was a Lead Research Scientist at Amazon, Seattle, where he built sequential decision making algorithms for 5+ business units. He got an MS from Georgia Tech (Stochastic Optimization) and MBA from INSEAD.

Pranay Karnany (CTO) has served as an Engineering Leader at multiple companies like Amazon, Vancouver (where he lead the org responsible for engineering high-frequency decision systems in the retail division), Fabric, Vancouver and Grab, Singapore. He graduated with a Computer Science major from IIT Guwahati.

Who You Are:

We are seeking a highly motivated and innovative Lead Research Scientist to join our dynamic team. You are passionate about solving complex problems and leveraging machine learning techniques to develop efficient solutions that scale. 

You have a strong foundation in natural language processing (NLP), small data and causal analysis.

Responsibilities:

• Conceptualize, develop, and deploy machine learning models underneath our business analysis tools, with an emphasis on statistical analysis of small data, and classification models trained on <100MBs of data. 
• Collaborate across the entire lifecycle of ML model development, from problem definition and data exploration to model training, validation, and deployment, ensuring that models are robust and reliable even with limited data.
• Conduct A/B testing and utilize statistical methods to evaluate model effectiveness, prioritizing robustness and reliability in scenarios with small data.

Requirements:

• Master’s degree or Ph.D. in Machine Learning, econometrics or a related quantitative field.
• Experience working with small data (including choice modeling) and causal analysis, as well as classification models.
• Experience in building and deploying production-level machine learning models.
• Proficiency in Python and experience with ML libraries such as TensorFlow or PyTorch.
• Familiarity with cloud platforms like GCP or Azure.
• Knowledge of software development principles, data structures, and algorithms.
• Excellent problem-solving skills and attention to detail.