Data Scientist

Job description

• Develop advanced ML/AI models leveraging both structured and unstructured datasets for batch and online inferencing use cases within the Customer Support and Employee Experience domains..
• Leverage or build analytics tools that utilize the data pipeline to provide significant insights into customer case data, bug data, operational, and other key business performance metrics..
• Collaborate with partners including the executive, product, data, and operations teams to transform business priorities into ML/AI problems and develop solutions..
• Work with MLOps specialists to manage the full life cycle of model development from concept to production..
• Collaborate with data and analytics specialists to strive for greater functionality in our data systems..
• Identify trends and patterns from datasets to scope opportunities for automation..
• Qualifications And Desired Experiences.
• 7+ years of experience in an AI Engineer role, with a Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field..
• 6+ years of experience in end-to-end architecting of advanced ML and AI solutions..
• Strong hands-on coding skills (preferably in Python) for processing large-scale data sets and developing machine learning models leveraging both structured and unstructured data..
• Experience working in a technical support environment, handling datasets from CRM, bug systems, and logs..
• Experience supporting and working with multi-functional teams in a multidimensional environment..
• Good team player with excellent interpersonal, written, verbal, and presentation skills..
• Create and maintain optimal data pipeline architecture, assembling large, sophisticated data sets that meet functional/non-functional business requirements..
• Experience working with Large Language Models, Generative AI, and Conversational AI..
• Familiarity with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow, and NLP libraries..
• Experience working with Databricks and Snowflake platforms..
• Experience with AWS, S3, Spark, Kafka, and Elastic Search..
• Experience with AWS cloud services: EC2, EMR, RDS, and Redshift..
• Experience with stream-processing systems: Storm, Spark-Streaming, etc..
• Experience with big data tools: Hadoop, Spark, Kafka, etc..
• Experience with relational SQL and NoSQL databases, including Postgres and Cassandra..
• Familiarity with building and optimizing data pipelines, architectures, and data sets..
• Familiarity with MLOps practices and Agile development framework..
• Familiarity with CRM platforms such as Salesforce..
• Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.