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Job Location: Bangalore/Bengaluru
Our leading data management and integration organization is currently in need of a Data Scientist to join our fast-growing team. The ideal candidate will be intricately involved in running analytical experiments in a methodical manner, and will regularly evaluate alternate models via theoretical approaches. This is the perfect opportunity for the successful candidate to become a part of an innovative and energetic team that develops analysis tools which will influence both our products
Job Responsibilities
- ML Pipeline Automation.
- Continuous Integration for Machine Learning projects.
- Continuous Delivery for Machine Learning projects.
- Improve and advance DataOps and MLOps infrastructure and operational processes.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
- Develop company A/B testing framework and test model quality.
Primary Skills
DataScientist – R, Python and SQL
MLops tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC)
Experience
8 to 12 Years
- Strong problem solving skills with an emphasis on product development.
- Experience with Jenkins or other build tools.
- Experience with ML Inference servers such as Triton, KFServing, or Seldon Core.
- Experience with Kubeflow, MLFlow or Sagemaker.
- Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
- Experience working with and creating data architectures.
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
- Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
Shift Timing
Shift B – 1:00 PM to 9:30 PM IST
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