Job Description :

A well-rounded Data Science Engineer who will be responsible for conceptualizing solutions to provide meaningful insights on data leveraging data analytics, statistical modeling and machine learning. Should have a strong background in statistical modeling and techniques and data-science/ML concepts, techniques, methodologies and tools.

Should have the ability to interface with stakeholders and SMEs as well as work independently within a team of data analysts, engineers and scientists to achieve target outcomes.

Responsibilities:

• Cleansing of large unstructured data and enabling analytical capability in order to query the data and address various business needs.
• Use unstructured and disjointed data sets for the purpose of independently generating actionable business insights as well as creating manageable analytical processes within the Data and Analytics group
• Build econometric and statistical models for various problems inclusive of projections, classification, clustering, pattern analysis, sampling, simulations, and so forth
• Predictive statistical models, segmentation analysis, customer profiling, analysis, survey design, and data mining.
• Research new ways for predicting and modeling end-user behavior as well as investigating data summarization and visualization techniques for conveying key applied analytics findings
• Perform ad-hoc data mining and exploratory statistical tasks on very large data-sets related to the business s strategies.
• Prepare reports and presentations for senior data scientists and relevant stakeholders that will give insights for departmental as well as business wide decision making
• Experiment with various technologies and tools with the vision of creating innovative data driven insights for the business at the quickest pace possible
• Take initiative in evaluating and adapting new and improved data analytics approaches for the business, working with senior management for approval

Technical Skills Required:

• Experience in data cleansing and preparation concepts and tools
• Programming Languages: Hands-on with at least 3 of the following: of Python, R, Perl, Java, Scala
• Strong background in relational databases and SQL
• Experience working with statistical libraries and frameworks for predictive analytics
• Experience with ML modeling tools and libraries (eg. TensorFlow)
• Knowledge or experience working with Jupyter or Zeppelin notebooks
• Knowledge of Hadoop will be a plus but is not required
• Experience and knowledge in the various types on neural net models (DNN, CNN)
• Expertise in probability and statistics, time-series analysis, or econometrics as well as experience in the use of machine learning methods, for example, linear regression, correlation, statistical significance, and so forth.
• Experience in data visualization concepts and tools
• Experience in data warehousing and dimensional data modeling is an added plus but not required
• Experience in NLP techniques, concepts and libraries will be an added plus but is not required