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Job Description & How to Apply Below
Amdocs helps those who build the future to make it amazing. With our market-leading portfolio of software products and services, we unlock our customers' innovative potential, empowering them to provide next-generation communication and media experiences for both the individual end user and enterprise customers. Our employees around the globe are here to accelerate service providers' migration to the cloud, enable them to differentiate in the 5G era, and digitalize and automate their operations.
Listed on the NASDAQ Global Select Market, Amdocs had revenue of $5.00 billion in fiscal 2024. For more information, visit
Location:
Pune
Experience:
6 to 10 Years
Must-Have
Skills:
Generative AI
Python
RAG (Retrieval-Augmented Generation)
LLM / NLP
EDA (Exploratory Data Analysis)
Cloud Platforms (AWS, Azure, GCP)
Transformers
Explainable AI
Deep Learning
Desired Background
At least 4+ years of relevant experience and track record in Data Science:
Machine Learning, Deep Learning and Statistical Data Analysis.
MSc or PhD degree in CS or Mathematics, Bioinformatics, Statistics, Engineering, Physics or similar discipline.
Strong hands-on experience in Python with the focus being on statistical algorithms development and GenAI practices.
Experience with data science libraries such as: sklearn, pandas, numpy, pytorch/tensor flow
Experience with GenAI concepts (RAG, LLM) and Agentical development: from conversional to autonomous agents
Team player, able to work in collaboration with subject matter experts, with ability to clearly present and communicate findings.
Proven ability to build and deliver data solutions in a short time frame.
Experience with Azure, docker, and development methodologies - an advantage
Proven experiences in productions and DevOPS practices
nd find the right answers.
Qualifications
Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)
At least 1 - 2 years' of experience in quantitative analytics or data modeling
Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms
Fluency in a programming language (Python, C,C++, Java, SQL)
Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau)
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