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Senior Data Scientist

DATA ANALYTICS
Pune, Hyderabad, Bangalore
About Us
SG Analytics (SGA), a Global Insights and Analytics company, focusses on ESG, Data Analytics, and Investment & Market research services. The company has presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad and growing consistently for the last few years.

SGA is a Great Place To Work (GPTW) certified company, and with its thriving work environment shaped by a growth mindset, abundant learning & collaboration opportunities, and a meritocracy-driven culture, SG Analytics has also been awarded regional best employer in 2016, 2018 & 2020.
Job Description
Business Requirements
  • Must be business Centric and think about the deliverable’s ROI.
  • Should be well-versed with data visualization to present the post-model results to Business stakeholders.
  • Should be well-versed with data visualization to present the post-model results to Business stakeholders
  • Ability to document the results in an intuitive manner for the stakeholders' understanding.

Data science Requirements:
  • Ability to understand and solve Data Quality Issues.
  • Have previously worked on building models related to recommender systems, Targeted Marketing campaign models for win-back campaigns.
  • Have an understanding of  Model deployment Life Cycle and data engineering.
  • Have an understanding of building model re-training pipelines for Model and data versioning along with incorporating test cases within the ML pipeline (In-variance Testing, Pre-model testing, and Post model testing).
  • Strong exposure to Machine Learning Algorithm usage and hyper-parameter tuning to optimize the model for deploying to production environments (GridSearch, K-fold Cross Validation).
  • Have an understanding of real-time data retrieval for In-app events data analysis.
Tech Skills:
  • Have a very good working knowledge of Pyspark/Python and SQL.
  • Good to have an understanding of Git-related concepts, Github actions.
  • Good to have AWS components knowledge related to data engineering and model deployment.
  • Hand-on experience in working on ML techniques such as clustering techniques, classification models, and propensity models.
  • Have a very good working knowledge of Pyspark/Python and SQL.
  • Good to have an understanding of Git-related concepts, Github actions.
  • Good to have AWS components knowledge related to data engineering and model deployment
  • Hand-on experience in working on ML techniques such as clustering techniques, classification models, propensity models.

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