Infosys is seeking a Lead Data Scientist with Machine Learning (ML), AI and Python experience. Ideal candidate is expected to have prior experience in end-to-end implementation of Machine Learning models that includes identification of right’ problem, designing optimum’ solution, implementing using best in class’ practices and deploying the models to production.
Will work in alignment with data strategy at various clients, using multiple technologies and platforms.
Required Data Scientist Qualifications :
- Bachelor’s Degree or foreign equivalent will also consider three years of progressive experience in the specialty in lieu of every year of education.
- At least 8 years of Information Technology experience
- At least 1 years of hands-on data science with machine learning
- Experiences with Python or R
- Experience with data gathering, data quality, system architecture, coding best practices
- Experience with Lean / Agile development methodologies
- This position may require travel, will involve close co-ordination with offshore teams
- This position is located in Richardson, TX or Dallas, TX and may require relocation to the area
Infosys encourages U.S. Citizens, lawful permanent residents, and those lawfully authorized to work in the U.S. to apply
Preferred Data Scientist Qualifications :
- 4 years of hands-on experience with more than one programming language; Python, R, Scala, Java, SQL
- Deep Learning experience with CNNs, RNN, LSTMs and the latest research trends
- Experience or Knowledge with Generative AI and working with any Large Language Models.
- Prior experience in cognitive services provided by various platforms such as AWS, GCP, Azure, IBM Watson
- Prior experience in Azure chatbot, Google DialogFlow, Alexa, RASA, Amazon Lex
- Experience with perception (e.g. computer vision), time series data (e.g. text analysis)
- Big Data Experience strongly preferred, HDFS, Hive, Spark, Scala
- Data visualization tools such as Tableau, Query languages such as SQL, Hive
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
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