- JOB TYPE: Freelance, Contract Position (no agencies/C2C - see notes below)
- LOCATION: Remote - United States only
- HOURLY RANGE: Our client is looking to pay $90 – $110/hr
- ESTIMATED DURATION: 40h/week - Long-term
THE OPPORTUNITY
What you’ll be working on
Our client is looking for a Data Scientist to join their diverse team of Engineers, Economists, Computer Scientists, Mathematicians, Physicists, Statisticians and Actuaries tasked with mining their industry-leading internal data to develop new analytics capabilities for their businesses.
The role requires a rare combination of sophisticated analytical expertise; business acumen; strategic mindset; client relationship skills, project management; and a passion for generating business impact.
Responsibilities:
• Develop and maintain consultative relationships with key business stakeholders
• Identify, source, transform and join public, proprietary and internal data sources
• Model large structured and unstructured data sources (e.g. financial transactional, time-series, text, speech/audio and image)
• Implement advanced statistical methods for prediction and optimization including a wide variety of machine learning technologies (logit, regression, decision trees/forests, boosted models, clustering, etc.) for purposes including explorative analysis, survival analysis, segmentation, prediction and recommendation systems
• Perform analysis and implement solutions that maximize business impact
• Prepare and present written and verbal reports to key stakeholders
• Some domestic travel may be required
• Execute all aspects of an advanced analytical project under guidance
Qualifications:
• Advanced degree (Masters or Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines
• Master’s degree graduates should additionally have at least two years of industry experience with responsibility for developing advanced quantitative, analytical, statistical solutions
• Hands-on experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data
• Experience using tools such as Python, R, or equivalent for statistical modeling of large data sets
• Well-developed written and oral communication skills with ability to present complex statistical concepts to non-analytical stakeholders (Excel, Word and PowerPoint are a must)
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