Principal Data Scientist Recommender System Job in Tvs Motors
Principal Data Scientist Recommender System
Tvs Motors
4+ weeks ago
- Bengaluru, Bangalore Urban, Karnataka
- Not Disclosed
- Full-time
Job Summary
Key Responsibilities
1) Data Science : a. Build ML/AI models leveraging a strong understanding of Machine Learning principles including standard algorithms for Regression and Classification, Deep Learning constructs (RNN, CNN, RBMs, Auto Encoders, GANs) and AI systems such as Voice to Text, NLP/NLU and Recommender systems b. Build recommender systems around agent Collections prioritization, product recommendations, and personalization engines c. Build sophisticated pricing engines based on AI powered product evaluation tools d. Build cross-sell/up-sell recommender systems using deep learning frameworks, and with sparse data e. 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 systemsf. Able to build standard ML systems using available ML components provided by AWS, Azure and GCP.
2) Cloud & Big Data:a. Work on Cloud Infrastructure(Azure, AWS) to provision data to ML models, build ML systems on deploy them at scale b. 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 on-Prem c. Put in place systems to continuously monitor, evaluate and re-train ML models in production
3) Build Production level Models @ scale a. Design and implement the machine learning lifecycle at scale, from building the data infrastructure to train/test Machine learning models to their production environments. b. Management of production ML workflows ensuring automated CI/CD capabilities are built into the work flow c. Design and Implement alerts/dashboards to ensure continuous monitoring of production models effectiveness ( accuracy, latency, performance etc.,)
Job Requirements
1) Ability Work closely with Data Analysts, Data Scientists and Business Analysts 2) Comfortable to work in cross-functional team and collaborate with peers during the project lifecycle3) Open to travel based on the project and teams locations.
Qualifications
BE/B.Tech/BS/MS/PhD in Computer Science or a related field from a Premier institute ( Tier 1 IITs, BITS and Top NITs, Ivy League US Schools)
Experience
1) 4 - 8 years of work experience as a Data Scientist with at least 4-5 years designing and implementing ML platforms for enterprise-grade ML use cases 2) Start-up experience is a plus
Functional Competencies
1) Masters or Ph.D. in computer science, mathematics, or statistics 2) Strong Data Science experience working with Java, Python, and R 3) Experience with standard ML algorithms, deep neural networks, Gaussian processes, and reinforcement learning4) A solid understanding of both probability and statistics 5) A firm understanding of mathematics (including the role of algorithm theory in machine learning and complex algorithms that are needed to help machines learn and communicate)6) Strong analytical and problem solving skills 7) Experience working with large amounts of data in a high throughput environment 8) Experience working with cloud platforms ( Azure, AWS, GCP) 9) Experience working with messaging tools like Kafka, Kinesis, Spark Streaming 10)Extensive knowledge of machine learning evaluation metrics and best practices
- Organization & Role
Key Responsibilities
1) Data Science : a. Build ML/AI models leveraging a strong understanding of Machine Learning principles including standard algorithms for Regression and Classification, Deep Learning constructs (RNN, CNN, RBMs, Auto Encoders, GANs) and AI systems such as Voice to Text, NLP/NLU and Recommender systems b. Build recommender systems around agent Collections prioritization, product recommendations, and personalization engines c. Build sophisticated pricing engines based on AI powered product evaluation tools d. Build cross-sell/up-sell recommender systems using deep learning frameworks, and with sparse data e. 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 systemsf. Able to build standard ML systems using available ML components provided by AWS, Azure and GCP.
2) Cloud & Big Data:a. Work on Cloud Infrastructure(Azure, AWS) to provision data to ML models, build ML systems on deploy them at scale b. 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 on-Prem c. Put in place systems to continuously monitor, evaluate and re-train ML models in production
3) Build Production level Models @ scale a. Design and implement the machine learning lifecycle at scale, from building the data infrastructure to train/test Machine learning models to their production environments. b. Management of production ML workflows ensuring automated CI/CD capabilities are built into the work flow c. Design and Implement alerts/dashboards to ensure continuous monitoring of production models effectiveness ( accuracy, latency, performance etc.,)
Job Requirements
1) Ability Work closely with Data Analysts, Data Scientists and Business Analysts 2) Comfortable to work in cross-functional team and collaborate with peers during the project lifecycle3) Open to travel based on the project and teams locations.
Qualifications
BE/B.Tech/BS/MS/PhD in Computer Science or a related field from a Premier institute ( Tier 1 IITs, BITS and Top NITs, Ivy League US Schools)
Experience
1) 4 - 8 years of work experience as a Data Scientist with at least 4-5 years designing and implementing ML platforms for enterprise-grade ML use cases 2) Start-up experience is a plus
Functional Competencies
1) Masters or Ph.D. in computer science, mathematics, or statistics 2) Strong Data Science experience working with Java, Python, and R 3) Experience with standard ML algorithms, deep neural networks, Gaussian processes, and reinforcement learning4) A solid understanding of both probability and statistics 5) A firm understanding of mathematics (including the role of algorithm theory in machine learning and complex algorithms that are needed to help machines learn and communicate)6) Strong analytical and problem solving skills 7) Experience working with large amounts of data in a high throughput environment 8) Experience working with cloud platforms ( Azure, AWS, GCP) 9) Experience working with messaging tools like Kafka, Kinesis, Spark Streaming 10)Extensive knowledge of machine learning evaluation metrics and best practices
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