Data Scientist
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About The Position
Infosys is seeking a Senior Lead Analyst with expertise in Machine Learning (ML), AI, and Python. The ideal candidate will have a strong background in the end-to-end implementation of Machine Learning models, which includes identifying the right problems, designing optimal solutions, implementing best practices, and deploying models into production. This role will require alignment with data strategies across various clients, utilizing multiple technologies and platforms. The candidate will be expected to work closely with offshore teams and may require travel to various locations including Raleigh, NC; Richardson, TX; Tempe, AZ; Phoenix, AZ; Charlotte, NC; Houston, TX; and Alpharetta, GA, or be willing to relocate. The position demands a Bachelor's Degree or a foreign equivalent, along with three years of progressive experience in the specialty in lieu of every year of education. Candidates should have at least four years of experience in Information Technology, with a minimum of one year of hands-on data science experience involving machine learning. Proficiency in Python or R, data gathering, data quality, system architecture, and coding best practices is essential. Familiarity with Lean and Agile development methodologies is also required. Preferred qualifications include four years of hands-on experience with multiple programming languages such as Python, R, Scala, Java, and SQL, as well as deep learning experience with CNNs, RNNs, LSTMs, and the latest research trends. Knowledge of Generative AI and experience with cognitive services from platforms like AWS, GCP, Azure, and IBM Watson is highly desirable. Experience with Azure chatbots, Google DialogFlow, Alexa, RASA, and Amazon Lex, along with skills in computer vision, time series data analysis, and Big Data technologies such as HDFS, Hive, and Spark, will be advantageous. Proficiency in data visualization tools like Tableau and query languages such as SQL and Hive is also expected. Good applied statistics skills, including knowledge of distributions, statistical testing, and regression analysis, are essential for this role.
