This is a hands-on, embedded AI, full development lifecycle role which provides an opportunity to solve complex machine learning problems using exciting cloud native technologies. This is a role where you can take responsibility in leading the technology effort from technical requirements gathering to final successful delivery of the product. You will work with a new technology team and have to opportunity to engage with smart and talented people across the Customer Data & Product Engineering teams.

Role Summary and Responsibilities

• Use statistical and machine learning techniques to create scalable analytics solutions within the customer 360 space.
  Establish scalable, efficient, automated processes for large scale data analysis, model development, model validation and model implementation.
• Design and architect modular and reusable code both for use in production environments Work with cross-functional Engineering, Analytics, and Data Science to define the KPIs for machine learning projects.
  Stay abreast of developments in research methodology and changing technologies in the marketplace and proactively identify applications of these latest developments to improve existing methods.
• Prepare and present findings to both technical and non-technical audiences.

Desired Candidate Profile

• 5+ years of production experience working in Data Science, Machine Learning or Software Engineering within finance, marketing or other high data volume domains
  Proficiency in developing applications with at least one compiled language (Java, C++, GoLang, Rust).
• Experience with cloud-native architecture and container orchestration (particularly Docker and Kubernetes) preferred.
  Experience utilizing Git, CI/CD, and MLOps frameworks for best practice code management and collaboration.
• Solid production experience using Python (including NumPy), PySpark and SQL and expert level production experience with Apache Spark, MLLib, and GraphX.
• Experience using AWS to build end to end distributed technical solutions (ALB, ECS, EC2, Fargate, Lambda,etc.) and as well as general cloud native applications.
  Strong fundamentals in problem solving, algorithm design and complexity analysis. Hands-on experience with web APIs, dashboarding and data visualization tools is a plus. Familiarity with streaming frameworks (RabbitMQ, Kafka, Kinesis) as well as relational (MySQL/Postgres) and streaming analytics frameworks (KQL, Spark Streaming, Flink) is a plus.
• Broad knowledge of relational (MySQL, Postgres, Athena, Redshift) and non-relational (Mongo, Cassandra, ElasticSearch) and databases, caching technologies (Redis, Memcache), serialization formats (Parquet, Avro, Arrow), and their use in designing and optimizing ML systems is required.
• Experience with Searching Technology like Solr or Elastic Search is a plus