Profound knowledge of deep learning principles and architectures, including CNNs, RNNs, and transformers, with the ability to apply these techniques to natural language processing tasks.
In-depth understanding of the workings of LLMs and the ability to manipulate model parameters to achieve desired outcomes in text generation.
Expertise in crafting effective prompts that guide AI models to generate desired outputs. Understand how different prompt structures influence AI behavior.
Experience with RAG models, which combine a retrieval component with a generator to enhance the quality and relevance of the AI's output. Understand how to effectively integrate external knowledge sources into AI responses.
Capability to train and fine-tune models on specific datasets to improve performance and ensure the relevance of the outputs to the task at hand.
MS degree in Statistics, Math, Data Analytics, or a related quantitative field
5+ years Professional experience in Advanced Data Science, such as predictive modeling, statistical analysis, machine learning, text mining, geospatial analytics, time series forecasting, optimization
Demonstrated Experience with NLP and other components of AI
Experience implementing AI solutions
Experience with one or more Advanced Data Science software languages (Python, R, SAS)
Proven ability to deploy machine learning models from the research environment (Jupyter Notebooks) to production via procedural or pipeline approaches
Experience with SQL and relational databases, query authoring and tuning as well as working familiarity with a variety of databases including Hadoop/Hive
Experience with spark and data-frames in PySpark or Scala
Strong problem-solving skills; ability to pivot complex data to answer business questions. Proven ability to visualize data for influencing.
Comfortable with cloud-based platforms (AWS, Azure, Google)
Experience with Google Analytics, Adobe Analytics, Optimizely a plus
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