Principal Data Scientist

1 week ago


Bengaluru, Karnataka, India TVS Motor Full time
Department

  • D&AI
  • Common Engineering, DPM
  • CE
  • Job posted on


Jul 18, 202
  • Employee Type
  • White Collar
  • Experience range (Years)
  • 10 years 13 years

Group Company:
TVS Motor Company

Designation:
Principal Data Scientist (PDS_D&AI
  • Common Engineering, DPM
  • CE)

Office Location:
Electronic City (Territory), Karnataka Koramangala (Territory)

Position description:

Need to deliver solutions to production through the software development lifecycle

Provide leadership to establish world-class ML lifecycle management processes.

Write production ready code and deploy real time ML models; expose ML outputs through APIs

Partner with data/ML engineers and vendor partners for input data pipes development and ML models automation

Good to have hands-on experience with open source tools such as Kaldi, Py torch-Kaldi.

Primary Responsibilities:

  • Build ML/AI models leveraging a strong understanding of Machine Learning principles including standard algorithms for Regression and Classification, Deep Learning constructs
  • Build recommender systems around agent Collections prioritization, product recommendations, and personalization engines.
  • Build sophisticated pricing engines based on AI powered product evaluation tools.
  • Build crosssell/upsell recommender systems using deep learning frameworks, and with sparse data.
  • Able to understand the determinants of success for a Machine Learning system, Model accuracy and efficiency, Data requirements, Training and Test constructs, CI/CD for ML systems.
  • Able to build standard ML systems using available ML components provided by AWS, Azure and GCP.
  • Cloud & Big Data:
  • 1. Work on Cloud Infrastructure (Azure, AWS) to provision data to ML models, build ML systems on deploy them at scale.
  • 2. Build ML systems using Spark libraries such as ML Lib, Spark SQL and be able to deploy them on clusters/machines, both on Cloud and onPrem.
  • 3. Put in place systems to continuously monitor, evaluate and retrain ML models in production.
  • 1. Design and implement the machine learning lifecycle at scale, from building the data infrastructure to train/test Machine learning models to their production environments.
  • 2. Management of production ML workflows ensuring automated CI/CD capabilities are built into the workflow.
  • 3. Design and Implement alerts/dashboards to ensure continuous monitoring of production models' effectiveness (accuracy, latency, performance etc.,)

Educational qualifications preferred.
-
Category: bachelor's degree, master's degree
-
Degree: Master's Degree, Bachelor's degree

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