Junior Research Scientist
6d ago

Who are we?

Afiniti is the world’s leading applied artificial intelligence and advanced analytics provider. Afiniti Enterprise Behavioral Pairing™ uses artificial intelligence to identify subtle and valuable patterns of human interaction in order to pair individuals on the basis of behavior, leading to more successful interactions and measurable increases in enterprise profitability.

Afiniti operates throughout the world, and has measurably driven billions of dollars in incremental value for our clients.

Key Responsibilities :

  • Develop modeling and validation tools to support the AI, production team.
  • Work with researchers to write real-time call-tracking, pairing, and task assignment systems in addition to our modeling and monitoring infrastructure.
  • Apply a wide range of classical to cutting-edge techniques often with extensive tailoring to build models and statistically validate and compare their predicted performance.
  • Carry out a careful analysis of complex environments and the ramifications of a change to our real-time pairing and recommendation systems.
  • Work intimately with our production data scientists, so the role can involve assisting with particularly tricky client models and developing new strategies to address complicated metrics or routing structures.
  • Compare performances and models around different modeling tools and techniques (Short term) and enhance their performance.
  • Experience and Skill Set :

  • Experience with statistics, machine learning, linear programming, or mathematical optimization, both practical and theoretical
  • Excellent skills at distilling complex, ambiguous scenarios into tractable models
  • Familiarity with simulation and at least one programming language for data analysis such as R, Python, or Julia
  • Familiarity with SQL, relational databases, version control, and tools for reproducibility such as git, Jupyter, and R Markdown, make or authoring custom packages
  • Demonstrated ability to manage time independently and take projects to completion
  • Willingness to both teach others and learn new techniques
  • Ability to document and explain cutting-edge techniques to other team members
  • Comfort working in a collaborative environment with cross-team communication to bring projects into production
  • Preference
  • Familiarity with basic Bayesian statistics, Regression trees, and sampling algorithms like MCMC.
  • Familiarity with optimization, reinforcement learning, dynamic programming, multi-armed bandits.
  • Publications in machine learning, statistics, or optimization.
  • Education and Qualifications

  • Graduate degree in Statistics, Mathematics, Econometrics, Operations Research, or other fields with relevant research
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