Data Scientist III

Kansas City | Chicago Data Driven Enterprise - Insights Full Time

Valorem Reply is seeking Data Science professional with nine years of experience in IT with focus in data analytics and business intelligence. Designed and implemented statistical / predictive models using machine learning techniques to deliver insights to business. Strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Responsibilities

  • Provides deep data science and related industry knowledge and consultation on strategically relevant technical areas (including latest state of the art from industry and academia).
  • Drives major data science-based projects and initiatives with high impact across multiple organizations.
  • Develops value propositions, identifying new business opportunities for data science-based innovations / solutions.
  • Demonstrates data science expertise to foresee and solve business problems to create new growth. Provides consultation as data science expert in internal as well as client engagements.
  • Establishes a strong data science network externally with academia or external organizations.
  • Understand business requirements and convert into analytical solution.
  • Analyze large amounts of information to discover trends and patterns.
  • Develop data science algorithms and generate actionable insights as per business needs and work closely with cross capability teams throughout solution development lifecycle from design to implementation and monitoring.
  • Manage day to day leadership and stakeholder communication including development plan
  • Requirements

  • Experience Needed: 9+ years in the following technologies:
  • Python
  • R
  • Spark/PiSpark
  • Hadoop
  • Predictive Modelling and Analytics

  • Additionally:

  • Provides deep data science and related industry knowledge and consultation on strategically relevant technical areas (including latest state of the art from industry and academia).
  • Drives major data science-based projects and initiatives with high impact across multiple organizations.
  • Develops value propositions, identifying new business opportunities for data science-based innovations / solutions.
  • Demonstrates data science expertise to foresee and solve business problems to create new growth. Provides consultation as data science expert in internal as well as client engagements.
  • Establishes a strong data science network externally with academia or external organizations.
  • Understand business requirements and convert into analytical solution.
  • Analyze large amounts of information to discover trends and patterns.
  • Develop data science algorithms and generate actionable insights as per business needs and work closely with cross capability teams throughout solution development lifecycle from design to implementation and monitoring.
  • Manage day to day leadership and stakeholder communication including development plan.


  • Technical Skills
  • Discovery Activities:
  • Analyze customer data set to understand model.
  • Includes: Data format, types, size, structure, frequency
  • Analyze consumer segment data set to understand model.
  • Current state including source, format, types, size, structure, frequency.
  • Review dashboards
  • Current dashboard
  • Metrics / calculations / complexity
  • Data Collection Activities:
  • Identify required data.
  • Collect data from sources.
  • Combine data as required.
  • Data Processing Activities:
  • Data cleaning - Includes but is not limited to handling Nulls, duplicates, missing values, datetime conversions.
  • Data processing - Includes but is not limited to impute values, split/merge/extract columns, formatting, and record linkage.
  • Exploratory Data Analysis (EDA):
  • Inspect data properties.
  • Descriptive statistics
  • Identify patterns.
  • Prioritize questions.
  • Modeling and Analysis:
  • Feature selection
  • Feature creation
  • Model evaluation
  • Model training
  • Statistical analysis
  • Visualization

  • Kansas City | Chicago

    Data Scientist III