Data Scientist

Main Responsibilities

• Develop, validate, document, execute and measure mission-critical statistical and ML models, leveraging state-of-the-art quantitative and computational techniques
• Perform exploratory data analysis (EDA) and statistical analysis, identify trends, patterns, and correlations to perform features engineering, and support client-centric initiatives across a broad range of PE objectives, such as deal generation, due diligence, and exit analytics
• Build, test, and commit predictive models for execution in collaboration with business and technology stakeholders
• Collaboratively develop prototype solutions to demonstrate ideas and prove concepts
• Be proactive and independent and be able to deliver projects with minimum inputs from business stakeholders

Required Background

• Master’s degree in Mathematics, Statistics, and Engineering/Economics
• Over four years of experience in a similar role
• Experience in building and testing statistical/ML models covering supervised and unsupervised algorithms
• Knowledge of Python programming and experience in writing production-ready codes (Python PEP8) and clear comments (Python docstrings) and modularising code, object-oriented programming (OOP), version control, etc.
• Experience in Natural Language Understanding (NLU) and Natural Language Processing (NPL) model building and analytics; for e.g., the voice of customer and voice of employee analyses
• Experience in geospatial analysis, geocoding, measuring distances, and tagging locations of interest from sources, such as GoogleMaps, OpenMaps, and TomTom
• Hands-on experience in handling large structured and unstructured datasets
• Experience in performing EDA and features engineering for ML models
• Experience in applying models for financial use cases, preferably in PE
• Experience with AWS cloud services, such as S3, Athena, and CI/CD (CodePipeline, CodeBuild, and CodeDeploy)
• Strong communication skills (both written and oral) with a business and financial aptitude, ensuring effective and continuous communication with global stakeholders
• Experience in human capital analysis (on workforce data)
• Experience in customer segmentation unsupervised analysis and modeling
• Strong R programming skill
• Experience with AWS cloud services, such as S3, Athena, and CI/CD (CodePipeline, CodeBuild, and CodeDeploy)
• Experience in building data science frameworks and applications in the AWS cloud environment