Position : Data Scientist / Deep Learning Engineer Location : Atlanta - Alpharetta, GA / Basking Ridge, NJ/ Dallas-Irving, TX (Hybrid/Onsite) Long term position Important Keywords : Traditional Data Scientist with Deep learning model, Development models & Deployment, end-to-end pipeline, Churn modeling, propensity modelling, transformers. Candidate should be strong on SQL & Python,PyTorch,PySpark (Production coding), Python, SQL (Strong) day-to-day Queries, GCP, BigQuery, Cloud Technologies
We're looking for a Traditional Data Scientist to build and deploy machine learning and deep learning solutions that support business goals. The role spans the full model lifecycle, from data exploration and feature engineering through development, deployment, and monitoring.
Responsibilities - Build end-to-end data science pipelines, including data preparation, model development, deployment, and monitoring.
- Develop churn and propensity models, along with deep learning and transformer-based models.
- Write and maintain production-quality Python and SQL for day-to-day queries and data analysis.
- Process large datasets with PySpark and build deep learning solutions using PyTorch.
- Use GCP and BigQuery to develop and deploy cloud-based data and ML solutions.
- Collaborate with engineering and business teams to turn requirements into reliable models and actionable insights.
Required skills - Strong, hands-on Python and SQL skills.
- Experience developing and deploying machine learning and deep learning models in production.
- Experience with PyTorch, PySpark, and deep learning techniques.
- Experience in churn modeling, propensity modeling, and transformers.
- Experience with end-to-end ML pipelines and cloud technologies.
- Familiarity with GCP and BigQuery.
Benefits: Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.
The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.
