- Entry level
- No Education
- Salary to negotiate
Quadient DXP has developed a next-generation cloud based solution that delivers hyper-relevant and hyper-personal omni-channel user experiences.
Quadient helps companies deliver meaningful interactions with current and future customers.
A Neopost digital company, the Quadient portfolio of technology enables organizations to create better experiences for their customers through timely, personalized, optimized, and highly individualized communications for all channels.
Our solutions bring together and activate the entire organization in the name of customer experience, through better collaboration and visibility into the customer journey.
Quadient supports thousands of clients and partners worldwide in the financial services, insurance and service provider industries in their quest to achieve customer experience excellence via mobile, digital, social media and print technologies.
This role will leverage world-class data science, machine learning, and cloud-connected technologies to envision and develop algorithms, and deploy them into compelling, engaging, and personalized user experiences.
You will be responsible for optimizing and integrating machine learning algorithms into concept prototypes and hardening these prototypes into production-grade applications.
Specific tasks may include developing libraries and micro-services that incorporate machine learning algorithms into enhancing, enriching, recommendation, classification, and similar use cases.
You will collaborate with data scientists in understanding the research behind algorithms, engage with software production teams to gather production requirements, and refine algorithms to fit functional and operational requirements.
As part of the team, you will evangelize an intelligence-driven culture throughout the DXP division and enable products grounded in science and powered by AI which improve our customer's experiences.
Responsibilities: Work on servicing various areas in the company to enable and empower data driven business decisions.
Seek out which areas need what kind of data and what their analytical requirements are and work on getting that data in the correct data model Be the first and the best user in all aspects of the DXP Platform
- data loading, data modeling, search and administration and give pertinent feedback to improve the platform.
Lead and grow a team of data engineers and analysts Develop the skills of data team through mentorship and training Evangelize data driven decision making throughout the company Must have: A MS or PhD in Machine Learning, Computer Science, Electrical Engineering, or other related fields such as Bioengineering, Statistics, or Physics.
5+ years of experience in designing, architecting, developing and shipping production-quality machine learning applications spanning multiple domains such as speech, audio, text or sensors.
3+ years of programming experience in at least two of the following: Python, C++, Java, or Scala.
Comfort with object-oriented programming and standard software development practices such as agile development, git workflow and unit testing.
Excellent communication and presentation skills, and ability to explain deep technical results to diverse groups of stakeholders.
A life-long learner who is curious, has a passion for solving hard, ill-defined problems, has comfort taking initiative and who continuously seeks to improve their skills and understanding.
Good to have: Experience with deep learning architectures and frameworks such as TensorFlow, Keras or PyTorch.
Experience using cloud computing platforms such as AWS or GCP and distributed ML frameworks such as SageMaker, ML Engine, Apache Spark, Neo4j, Elasticsearch or Mahout.
Experience analyzing noisy time series such as biosensor data, EEG, IMU and/or other embedded sensor data using digital signal processing, autoregressive models and/or deep learning.
Experience analyzing audio signals.
Experience with deep learning approaches for audio data is a plus.
Experience with edge computing, particularly embedding machine learning and deep learning algorithms into hardware systems.
Experience in applying broader AI approaches, such as agent-based learning, active/reinforcement learning and probabilistic inference using knowledge graphs to user-in-a-loop use-cases.
Experience setting up and/or managing SQL and NoSQL databases (graph, key-value, document.)