Data Scientist with Bachelor’s Degree in Computer Science, Computer Information Systems, Information Technology, or a combination of education and experience equating to the U.S. equivalent of a Bachelor’s degree in one of the aforementioned subjects.

Job Duties and Responsibilities:

• Define end-to-end machine learning pipeline for large scale technology products and deep technical products in distributed processing, real-time and scalable systems.
• Develop solutions to business problems using the data science life cycle.
• Develop and maintain data analytics solutions and machine learning algorithms.
• Build and leverage new and existing tools for Natural Language Processing (NLP), and intelligent document processing tasks.
• Design and Develop Spark applications in Python for streaming multi-modal data like text, images, videos for distributed machine learning training.
• Design and Develop AWS Cloud deployment scripts using AWS Cloud Formation Templates, Terraform for deploying data and ML pipelines.
• Develop a Proof of Concept for multiple intents to demonstrate conversational flow, responses from an embedded document, and generative AI (Chat GPT).
• Fine tune applications and systems for high performance and higher volume throughput and Pre-Process using AWS Stack for data pre-processing.
• Translate Load and exhibit unrelated data sets in various formats and sources like AVRO, Parquet, JSON, Text files, Kafka queues and Log Data.
• Develop and implement Generative AI models, with a strong understanding of techniques such as GPT, T5, Stable Diffusion and BERT.
• Drive excellent management skills to deliver complex projects, including effort/time estimation, to build detailed work breakdown structure (WBS), to manage critical path, and to use PM tools and Platforms.
• Build Scalable Client engagement level processes for faster turnaround and higher accuracy.
• Run regular Project reviews and Audits to ensure that projects are being executed within the guardrails agreed by all Stakeholders.
• Manage the Client Stakeholders and their expectations with a regular cadence of weekly meetings and status updates.

Technologies/Environment involved :

• Distributed storage: AWS Cloud Storage (S3), Azure HD Insight, Google Cloud (GCP)
• Database management: Mongo DB, Cassandra, Postgres, Oracle, MS SQL Server, Redshift
• Graph Processing: Neo4J
• Machine learning: Spark Machine Learning Library (MLlib), TensorFlow, Keras, Pytorch
• Data processing: Spark, Hadoop MapReduce, Kafka and Storm, Airflow, Spark-streaming
• Programming Languages: Java, Scala, Python [REST Framework], PySpark
• DevOps Tools: BitBucket, Git, Apache Maven, Selenium, Jenkins, Docker