Sr Level Lead Data Scientist ( Sr Level )
About The Position
Understand the frameworks used currently in terms of recommender system pipelines Come up with ways to improve the current algorithms and pipelines, responsible for the end-to-end testing of the algorithm along with collaborating with the MLOps team to see the model through Dataset Gathering - Collect Data Transformer Architecture - 1. Customer Sequence Modeling, 2. Multi Modal and Variable Length Inputs Offline Model - 1. Build Offline Model, 2. Hyperparameter Tuning Build Online Inference Path - 1. Combine Offline with real time session features Data Quality - 1. Deployment, 2. Success Metric Improvement Job Description: Proficiency in statistical analysis and machine learning using tools such as Python, R, and SQL is essential. Strong understanding of data modeling and data visualization techniques is required, along with experience in big data technologies like Hadoop and Spark. Knowledge of data wrangling and preprocessing is necessary. Strong analytical and problem-solving skills are crucial for developing insights and solutions from complex datasets. Expertise in predictive analytics and applied machine learning is also needed. The ability to measure impact and effectively communicate data-driven stories is important for influencing decision-making processes.
Requirements
- Proficiency in statistical analysis and machine learning (e.g., Python, R, SQL)
- Strong understanding of data modeling and data visualization techniques
- Experience with big data technologies (e.g., Hadoop, Spark)
- Knowledge of data wrangling and preprocessing
- Strong analytical and problem-solving skills
- Strong predictive analytics and applied machine learning skills
- Ability to measure impact and data story telling
- 10+ years experience
Responsibilities
- Understand the frameworks used currently in terms of recommender system pipelines
- Come up with ways to improve the current algorithms and pipelines
- Responsible for the end-to-end testing of the algorithm
- Collaborating with the MLOps team to see the model through
- Dataset Gathering - Collect Data
- Transformer Architecture - 1. Customer Sequence Modeling, 2. Multi Modal and Variable Length Inputs
- Offline Model - 1. Build Offline Model, 2. Hyperparameter Tuning
- Build Online Inference Path - 1. Combine Offline with real time session features
- Data Quality - 1. Deployment, 2. Success Metric Improvement
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
