Machine Learning Engineer

• Play a critical role in the development and application of data science algorithms and advanced analytics techniques across a variety of use cases, including recommendation models, email personalization, and segmentation.
• Build models over various datasets, to analyze importance/centrality (Page Rank,) , similarity (KNN, TF-IDF, etc.)
• Design and analyze experiments across user experience on the application, as well as over email communications (A/B, multivariate)
• Perform analyses of user data and provide feature teams with an understanding of how users are interacting with their product
• Be able to take tasks that are at times, ambiguous and not so clearly defined, and find the specific requirements by communicating with the appropriate team leads
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Be able to clearly and concisely communicate to senior leadership the complexities of the models that have been built and how they will be used/impact end users.
• Approach issues and new feature development with creative solutions
• Be able to write code and properly manage versions and deployment across environments

Requirements

Must have skills:

• Strong experience working with data science/ML libraries in Python (SciPy, NumPy, TensorFlow, SciKit-Learn, etc.)
• Strong experience working in cloud development environments (especially Azure, ADF, PySpark, Scala, DataBricks Delta, R, SQL)
• Experience building data science models for use on front end, user facing applications, such as recommendation models
• Experience with REST APIs, JSON, streaming datasets
• Experience working with user behavioral data,such as web analytics (Google/Adobe Analytics)
• Experience building ML models and pipelines using MLflow, AirFlow.
• Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases.
• Experience with stream-processing systems: Storm, Spark-Streaming, etc.
• Understanding of Graph data, neo4j is a plus
• Strong understanding of RDBMS data structure, Azure Tables, Blob, and other data sources
• Experience with test driven development
• Experience in PowerBI or other tools
• Understanding of Jenkins, CI/CD processes using Git, for cloud configs and standard code repositories such as ADF configs and Databricks