Data Scientist III

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