You will work as part of our global Data Science team to provide data driven AI solutions for our customers using state-of-the-art methods and tools.
Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints
Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps
Create and maintain efficient data pipelines, often within clients’ architecture. Typically, data are from a wide variety of sources, internal and external, and manipulated using SQL, spark, and Cloud big data technologies
Assemble large, complex data sets from client and external sources that meet functional business requirements.
Build analytics tools to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
Perform data cleaning/hygiene, data QC, and integrate data from both client internal and external data sources on Advanced Data Science Platform. Be able to summarize and describe data and data issues
Utilize deep learning principles and architectures, including CNNs, RNNs, and transformers, apply these techniques to natural language processing tasks.
Manipulate model parameters to achieve desired outcomes in text generation.
Craft effective prompts that guide AI models to generate desired outputs. Understand how different prompt structures influence AI behavior.
Use RAG models, and 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.
Train and fine-tune models on specific datasets to improve performance and ensure the relevance of the outputs to the task at hand.
Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making
Document predictive models/machine learning results that can be incorporated into client-deliverable documentation
Assist client to deploy models and algorithms within their own architecture
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.
Deployment experience - Understanding of how to integrate models into production
Engineering experience
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
At least 3 years of post graduate 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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