Experience level required 10+ years
Mandatory required skills NLTK, SpaCy, Data Science Platforms (Dataiku, H2o, Azure)
Preferred/Desired skills Big Data, Hadoop, HIVE/HQL, HDFS, Spark/PySpark, Kafka
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 Timeseries and forecasting models.
Experience in Deep Learning model development using Tensorflow, Keras, PyTorch
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, SciPy and SciKit-learn