Senior Data Scientist, Machine Learning

Role Overview

• Prototype and train learning-based models using a data-centric approach, applying techniques such as automated feature engineering, active learning, and fine-tuning on curated datasets
• Design, develop, and maintain efficient data and feature extraction pipelines to support ML engineers in accessing high-quality data for model training
• Design auto labeling system using ensemble of models that can reason from multimodal data for different use-cases, including image semantic labeling using vision grounded models, intent and path prediction ground truth
• Perform complex data extraction, transformation, and loading (ETL) processes, ensuring data is clean, accessible, and well-documented
• Write and optimize high quality SQL queries for data analysis and ingestion from various sources
• Partner with data infrastructure and ML engineers to ensure seamless integration of data and machine learning workflows
• Produce highquality, maintainable code and participate in peer code reviews to share knowledge and uphold team standards

Requirements

• Bachelor’s Degree or U.S. equivalent in Computer Science, Data Science, or a related field
• 5 years of professional experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or any occupation, job title, or position performing software engineering and machine learning.
• 5 years of professional experience utilizing SQL to write and optimize complex queries for extraction, analysis, and ingestion of structured, semi-structured, and unstructured data
• 5 years of professional experience utilizing machine-learning frameworks (including TensorFlow and PyTorch)
• 5 years of professional experience designing and developing data and feature extraction pipelines, including pipelines for multi-modal data (including images, point clouds, or time-series)
• 5 years of professional experience training and prototyping machine-learning models using data-centric techniques including automated feature engineering, active learning, and fine-tuning
• 5 years of professional experience utilizing cloud platforms including AWS, GCP, or Azure
• 5 years of professional experience utilizing containerization and workflow tools including Docker, Kubernetes, or Airflow
• 5 years of professional experience collaborating with cross-functional engineering teams to integrate data pipelines and ML workflows
• 3 years of professional experience programming in Python, building scalable data pipelines, or implementing ETL workflows
• 1 year of professional experience working with relational databases and SQL (including Postgres, Redshift, or SQL Server)

Tech Stack

• Airflow
• Amazon Redshift
• AWS
• Azure
• Cloud
• Docker
• ETL
• Google Cloud Platform
• Kubernetes
• Postgres
• Python
• PyTorch
• SQL
• Tensorflow

Benefits

• Offers Equity