Data Scientist

TechDigital Group; San Diego (CA); On-site; USD 100,000 - 140,000; Full time

Job summary

A leading technology firm in California is seeking a skilled Machine Learning Engineer to lead the development of advanced machine learning models and design scalable data pipelines. The ideal candidate will have a Bachelor's or Master's degree in a relevant field and be proficient in Python, SQL, and tools like TensorFlow and PyTorch. This role includes mentoring junior engineers and implementing best practices in data science, offering an exciting opportunity to work with cutting-edge technologies.

Qualifications

• Bachelor's or Master's degree in relevant fields.
• Proficiency in Python, PySpark, and SQL.
• Experience with TensorFlow and PyTorch for ML and DL.

Responsibilities

• Lead development of machine learning models.
• Design scalable data pipelines using PySpark.
• Mentor junior engineers and guide best practices.

Skills

Python

PySpark

SQL

Pandas

Numpy

Machine Learning

Deep Learning

Data Analysis

Education

Bachelor's or Master's in Computer Science, Statistics, Data Science, Data Analytics, or Machine Learning

Tools

TensorFlow

PyTorch

AWS

SQL

Job description

• Lead development of advanced machine learning and statistical models
• 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
• Mentor junior engineers and lead code reviews, best practices and documentation
• Designing and implementing big data, streaming AI/ML training 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 initiative

Requirements

• Bachelor and/or Masters degree in either one of the disciplines: Computer Science, Statistics, Data Science, Data Analytics, Machine Learning
• Python, PySpark, SQL
• Pandas, Numpy, Excel, Plotly, Matplotlib, Seaborn, ETL, AWS and SageMaker
• Supervised learning models: Regression, Classification
• Unsupervised learning models: Anomaly detection, clustering
• Deep Learning Autoencoders, CNN, RNN, LSTM, hybrid models
• Model evaluation, cross validation, hyper parameters tuning
• Scikit-Learn, TensorFlow, PyTorch