Work with practice leaders and clients to understand business problems, industry context, data sources, potential risks, and constraints
Problem-solve with practice leaders to translate the business problem into a workable Data Science solution; propose different approaches and their pros and cons
Work with practice leaders to get stakeholder feedback, get alignment on approaches, deliverables, and roadmaps
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 Machine Learning methods and tools.
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
Conduct statistical data analysis, including exploratory data analysis, data mining, and document key insights and findings toward decision making
Train, validate, and cross-validate predictive models and machine learning algorithms using state of the art Data Science techniques and tools
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
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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