JobID_Data Scientist826

Involgix; California (MO); On-site; USD 120,000 - 170,000; Full time

Job summary

Involgix is seeking a data engineering/ data science professional to design scalable data pipelines using PySpark and to transform data with Pandas/NumPy/SQL. You will build, train, and fine-tune ML/DL models with TensorFlow and PyTorch on AWS SageMaker, and orchestrate end-to-end data workflows across AWS services.

You will translate complex business problems into data-driven solutions while promoting best practices in data science and governance, guiding strategic data initiatives and staying

Qualifications

• Experience designing scalable data pipelines using PySpark.
• Proficient in data transformation and exploratory analysis with Pandas/NumPy/SQL.
• Hands-on experience building, training, and fine-tuning ML/DL models (TensorFlow, PyTorch).

Responsibilities

• Design scalable data pipelines using PySpark.
• Perform data transformation and exploratory analysis with Pandas, NumPy and SQL.
• Build, train and fine tune ML/DL models with TensorFlow and PyTorch.
• Implement big data, streaming AI/ML, and prediction pipelines.
• Translate complex business problems into data-driven solutions.
• Promote best practices in data science and model governance.
• Work with Python, TensorFlow, PyTorch, SQL, and cloud platforms.
• Guide strategic data initiatives and stay current with evolving tech.
• Build, deploy and operationalize models on AWS SageMaker with monitoring for drift and performance.
• Develop and orchestrate ETL/ELT workflows across AWS services (Glue, S3, Lambda, Step Functions, Redshift, Athena).

Skills

PySpark

Pandas

NumPy

SQL

TensorFlow

PyTorch

AWS SageMaker

ETL/ELT

Model governance

Tools

AWS

Cloud platforms

Job description

• Design scalable data pipelines using PySpark
• Perform data transformation and exploratory analysis using Pandas, Numpy and SQL
• Build, train and fine tune machine learning and deep learning models using TensorFlow and PyTorch
• Implementing big data, streaming AI/ML and prediction pipelines.
• Translate complex business problems into data driven solutions.
• Promote best practices in data science, and model governance.
• Use tools like Python, TensorFlow, PyTorch, SQL, and cloud platforms
• Stay ahead with evolving technologies and guide strategic data initiatives.
• Build, deploy and operationalize models on AWS SageMaker — including training jobs, hyperparameter tuning, model registry, real-time and batch inference endpoints, and monitoring for drift and model performance in production
• Develop and orchestrate ETL/ELT workflows across AWS services such as Glue, S3, Lambda, Step Functions, Redshift and Athena to deliver clean, reliable, analysis- and feature-ready datasets for downstream modeling.