Lead Data Scientist

Published date Posted on Indeed on Jul 30, 2022 (21 d ago)

Alvaria employs a team of passionate individuals who are changing the face of customer engagement. Over our 40-year history we have empowered employees by creating an inspired community that values Urgency, Accountability and Results. Our ability to think big has enabled us to continually evolve and lead the market, and to stay on the forefront with exciting technologies including cloud, mobile and artificial intelligence.


Job Description

Join Avlaria’s AI and ML team where you will define and shape the future of Customer experiences using advanced machine learning and deep learning technologies across our SaaS product lines. Leveraging the power of AI & ML on top of Alvaria’s enterprise customer data is crucial to the success of their mission critical solutions. This role will be a key thought leader, working to successfully deliver AI/ML engines and services into production so that they can add value across Alvaria’s best-in-breed public cloud product offering. The ideal candidate would have previous, Machine Learning experience, understanding of GPU architecture, and be willing to learn about new technologies.

The role will report to the Director of AI and ML Engineering.

Responsibilities

  • Utilize time series modeling and build ML applications by training on large, time-series datasets for forecasting and build models for optimizing Call center scheduling
  • Partner with cross functional team members to develop and maintain a well-defined roadmap, while balancing technological excellence.
  • Help lead and develop near and long-term forecasting algorithms that solve key business opportunities across different industries
  • Work on projects that will have a direct business impact as they are significant to the company
  • Work closely with other data scientists, AI engineers, data engineers within the team as well collaborate with product managers, software engineers

Qualifications

Qualifications

  • 5+ years of hands-on experience building time series forecasting models leveraging machine learning, statistical, econometric, and/or hybrid models
  • 5+ years of experience with machine learning engineering and data science workflows
  • 5+ years of experience working on public cloud environments and associated deep understanding of ML tools and solutions within that stack, or open-source alternatives
  • Experience in using GPUs for training intensive compute jobs on extremely large datasets
  • Experience using modern Deep Learning software architectures and frameworks including Tensorflow or PyTorch
  • Implement best practices of curated data and evaluate ML models on very large-scale datasets
  • BS degree; MS or PhD in Computer Science, Statistics, Mathematics or Machine Learning preferred. We will consider equivalent practical experience.
  • Experience in delivering multiple complex technical projects within an Agile environment.
  • Experience with big data engineering tools such as Python, Spark, or comparable.
  • Experience creating and building models using ML libraries such as scikit-learn or comparable
  • Experience owning the entire lifecycle of a ML project from conception to deployment and monitoring
  • Develop high quality, maintainable and scalable production ready ML models.


Knowledge, Skills, And Abilities

  • Expertise in developing software on a public cloud platform (e.g. AWS, GCP, MS Azure etc.)
  • Expertise in developing software with stream processing technology (e.g. Kafka, AWS Kinesis etc.)
  • Proficiency with backend systems built using microservices, containerized infrastructure, and modern continuous delivery practices.
  • Outstanding written and verbal communication skills.


#LI-MO1

#LI-Remote

Alvaria is an equal opportunity/affirmative action employer with a strong commitment to diversity. In that spirit, we are particularly interested in receiving applications from a broad spectrum of people, including women, minorities, individuals with disabilities, veterans or any other legally protected group.


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