Data Scientist

Deal Score0
Deal Score0

Website The Carlyle Group

Position Summary

The Carlyle Group is looking for qualified individuals who are eager to solve difficult problems to join us1-on-1 mentorship, training and advice to help users land their next job. Pay only if you succeed in getting hired and start work at a high-paying job first. The newly created data science team falls under global private equityAd:

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We leverage modern techniques like big data, and machine learning to build industrial-level solutions to facilitate investment decision-making As part of a small team made of individuals from diverse backgrounds, we believe everyone is an integral part of the team’s successAd:

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Using the analogy of practicing alchemy, we will have math, computer science, and domain knowledge of finance at our disposal to create something truly valuable You will not only work with top talents within the private equity industry, but also work hand-in-hand with teammates previously work for top technology companies1-on-1 mentorship, training and advice to help users land their next job. Pay only if you succeed in getting hired and start work at a high-paying job first. Instead of fixing and maintaining large systems, you will be the pioneer to truly build something from scratch and put it into useAd:

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• “Make sure the data is always good”
• Work with stakeholders to understand the business context
• Independently conduct data discovery, descriptive and predictive analysis
• Hypothesis testing, statistical analysis and modeling (time series data with structured and unstructured data, graph analysis, supervised learning)
• Work with engineers to deploy solutions into production
• Develop solutions to ensure model output is explainable and interpretable
• Documentation and testing to ensure fully reproducible research


Education & Certificates
• College degree, required
• Masters or above in STEM field, preferred
• Professional Experience
• 3 ~ 6 years of hands-on Machine Learning experience

Competencies & Attributes
• Strong verbal and written communication skills
• Comfortable with public presentation and storytelling
• Curiosity with a can-do attitude
• Technical Requirements (following order of importance):
• Expert in mathematical theory and engineering practice behind common ml techniques (regression, classification, time series, neutral network etc)
• Familiarity with statistics, probability, and hypothesis testing
• Hands-on experience with ML framework (pytorch, tensorflow, scikit-learn)
• Python/R
• SQL (ANSI or common RDBMS)
• Linux
• Experience deploying models in production – online/offline inferencing
• Cloud computing (AWS preferred)
• Familiarity with finance – private equity, asset valuation

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