Data Scientist - ML, Python

Data Scientists: Machine Learning,Pytorch,SQL

(Exp: 10 to 12 years)

Job responsibilities:

• Responsible for coding and testing customized analytical models of medium to high degree of complexity.
• Must be able to implement best practices when writing and testing code.
• Familiarity with the life cycle of a data science project and extensive experience with python is a must.
• Requires strong interpersonal and communication skills, operational Elasticsearch knowledge and ability to construct GUI interfaces
• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs
• Proven experience in developing models using Regression, Decision trees, Bayesian networks, Random Forest, Logistic regression, Support vector machine, Gradient boosting algorithms, Clustering algorithms and Dimensionality Reduction Algorithms.
• Proven experience in building and implementing Time series and forecasting models.
• Experience in Deep Learning model development using Tensor Flow, Keras, Py Torch
• Hands on experience in building ML workflows
• Experience in Model implementation, Governance, and monitoring.
• Responsibilities include Performing the code development of complex analytic algorithms and paradigms, perform a clear breakdown of the analytic functionality and separate it into working modules.
• Working experience in Python, R and SQL
• Experience in Python data science packages such as numPy, Sci Py and Sci Kit-learn

• Fluent with Natural Language Processing and text analytics libraries (NLTK, Spa Cy)
• Experience with Data Science Platforms (Dataiku, H2o, Azure…)
• Experience with one of Subversion/SVN, Git, Git Hub, Git Lab, Mercurial, or other version control system
• Experience with cloud platforms AWS, Azure, GCP a plus
• Knowledge in Big Data, Hadoop, HIVE/HQL, HDFS, Spark/PySpark, Kafka
• Familiar working in an AGILE environment