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Full Stack Developer - User Personalization
Ref No.: 18-02378
Location: Chicago, Illinois
Start Date: 04/04/2018
Description:
Please see the attached job descriptions for each role.

When submitting candidates advise which of the attached postings they are being submitted for and the rate for the role.
Hours per Day 8
Hours per Week 40
Total Hours 1,560.00

Position title: Full Stack Developer - User Personalization
PURPOSE
We are currently seeking an experienced full stack developer who will be responsible to build and evolve our innovative product once launched. As a member of the team, you will collaborate with other data engineers, data scientists, analysts, and product owners to design to develop personalized meal planning solution. This role is analytical and creative and will be responsible for taking the experience from concept to launch.

Within the Food and Beverage Heinz corporate umbrella is our new department is focused on Online and Digital Growth. We are a big company but our group will operate as a startup. The team is focused on creating software application that personalizes meal planning experience for consumers. The role will work with a team of technology developers, culinary experts, and nutritionist to creating a revolutionary new user platform.
Location/Duration
  • Work location(s) – Based out of downtown Chicago (Client Center)
  • Duration of assignment – 4/2 to 8/24/18
  • Typical anticipated hours per week/schedule - 40
PRINCIPAL ACCOUNTABILITIES - List the activities the role requires on a regular basis. Identify the major end result the position is expected to achieve for each of these activities.
  • Consulting with internal stakeholders to understand technical needs, analyzing solution options and making a recommendation based upon your knowledge and experience
  • Leverage machine learning techniques to build systems a personalization logic which processes and derives insights from different data sources every day (Critical for role success)
  • Developing enterprise and consumer grade solutions
  • Gaining technical knowledge of software and open source technology stacks
  • Partnering with designers and user experience specialists to create consumer-first user experiences
  • Bridging the gap between elegant front-end design and existing enterprise backend architectures
KNOWLEDGE/EXPERIENCE/COMPETENCIES -What skills and knowledge are required for this job? What type of and how much prior experience is required to do this job?
Experience and Knowledge:
  • Previous professional experience working with Python, RabbitMQ, Django/ Flask, Rest Framework, Oauth, NLTK/SciKit and other Python packages
  • You have created and deployed microservices
  • Ability to use containers like dockers or kubernettes
  • Experience working with agile methodology & dev ops geared towards cloud
  • Strong knowledge of full stack development and Object Oriented development
  • Familiarity with IOT integrations (ex: Alexa skill development process)
  • Published apps, websites or other examples of solutions built
  • You have worked with AWS, Azure, Google Cloud (preferred)
  • You have front-end development experience: HTML5, JavaScript/ JQuery, CSS
  • Experience with Angular, node.js and templating engines like Handlebars, Ninja, or equivalent
  • You have worked with, and are proficient with Python ORMs; MVC pattern and frameworks; Rest API architecture, Postgres SQL and/or NoSQL databases like Cassandra or Mongo
  • iOS development experience helpful but not required
  • Previous experience writing personalization logic for consumer facing applications (nice to have)
  • Minimum of Bachelor degree preferably in MIS or equivalent IT field experience required.

Skills needed:
  • Strong communication skills
  • Strong data engineering background with prior experience in large-scale distributed data processing platforms such as Kafka, Spark, Kinesis
  • A desire to learn and share your knowledge
  • 2+ years of work or educational experience in Data Science / Machine Learning.
  • Strong knowledge of Client techniques including both supervised and unsupervised learning, classification, regression, and optimization