Data Scientist

What you will do:

 9 - 17 years of Telecom/IT experience in a Data related Role with AI ML and Data Science experience.

 At least 5 years’ experience using the following software/tools:

· Python Development for AI/ML and automation

· Data pipeline development in Elastic (ELK) Stack, Hadoop, Spark, Kafka, etc.

· Relational SQL and NoSQL databases, for example Postgres and Cassandra.

·MLOps experience including Model deployment and monitoring

·AI/ML Solutions for prediction, classification, Natural Language Processing

·Generative AI technologies like Large Language Models, Agentic AI, RAG and other developing technologies

 Proficiency in Python programming, with experience in Python libraries commonly used in data engineering and machine learning (e.g., pandas, numpy, scikit-learn)

· Hands-on experience with Kubernetes and containerization technologies (e.g., Docker).

· Proficiency in Linux & Shell scripting knowledge

· Demonstrable skills and track record in the use of modern machine learning frameworks such as Keras, TensorFlow, Scikit-Learn, Spark, Langchain, Langgraph etc. and visualization frameworks e.g. Matplotlib, Seaborn etc

· Demonstrable skills and track record in the use of NLP (Natural Language Processing) frameworks such as nltk, spacy etc.

· Good understanding of various ML concepts and the ability to apply the appropriate algorithms in various contexts and situations.

· Hands on experience with Generative AI technologies like Large Language Models, Agentic AI, RAG and other developing technologies.

· Good understanding of challenges faced when rolling out same ML model for different customers.

· Understanding of MLOps requirements for model management, monitoring, etc., in a production environment

· Familiarity with a Machine Learning Platforms and frameworks

· Familiarity with ElasticSearch, Logstash, and Kibana for log management and data visualization.

· Ability to work with high volumes of data, identifying predictive models, recommendation engines, anomaly detection systems, statistical models, deep learning, reinforcement learning and other machine learning systems.

· Knowledge of Data strategy including data quality parameters, data lineage, data catalogue, data architecture, data management, data governance and control etc

· Proficiency in design and development of data pipelines

· Experience in performing root cause analysis on internal and external data and processes to answer specific business questions and seek opportunities for improvement.

What you will Bring:

 Responsible to work hand in hand with business representatives to design, architect and deliver AI/ML and/or Generative AI solutions considering business ROI in perspective.

· Create and maintain optimal data pipeline architecture.

· Identify Data intensive and AL/ML Use cases for different existing Managed service accounts.

· Prepare technical presentation, design documents and demonstrations for customer presentations on Data strategy and AI/ML Solutions

Prepare design, solution documents for AI/ML Solutions including classic AI/ML and Generative AI based solutions.

· Assemble large, complex data sets that meet functional / non-functional business requirements.

· Design solutions that will keep data separated and secure across national boundaries through multiple data centers and strategic customers/partners keeping international security standards, organization and customer security       requirements in mind.

· Create toolchains for analytics and data scientist team members that will assist them in building and optimizing our product into an leading industrial product.

· Work with data and machine learning experts to strive for greater functionality in our data and model life cycle management systems.

· Have strong analytic skills related to work with unstructured datasets.

· Working knowledge of Generative AI technologies, Large Language Models (LLMs), Agentic AI architecture and implementation tools.

· Maintain clear and comprehensive documentation for data engineering processes, ELK configurations, machine learning models, visualizations, and deployments