Data Scientist Engineer

Role overview

Responsibilities

• check_circle AI/ML Model Development: Design and train machine learning models using various algorithms, including deep learning, NLP, and computer vision.
• check_circle <strong>Databricks</strong> Orchestration: Build and optimize end-to-end AI/ML pipelines on <strong>Databricks</strong>, utilizing Unity Catalog for governance and MLflow for experiment tracking.
• check_circle Generative AI & LLMs: Implement advanced AI patterns such as Retrieval-Augmented Generation (RAG) and fine-tune pre-trained models for specific enterprise tasks.
• check_circle Python Expertise: Write production-quality, idiomatic PySpark and Python code that leverages Spark’s distributed nature.
• check_circle Collaboration: Partner with Engineering and Product teams to translate business problems into scalable analytical solutions.
• check_circle Insight Extraction: Perform exploratory data analysis (EDA) and extract meaningful insights from massive, complex datasets to drive strategic decisions.

Basic qualifications

• Education: MS or PhD in a quantitative field such as Computer Science, Statistics, or Math.
• Experience: 5+ years of hands-on experience in data science or AI engineering in high-growth environments.

Preferred qualifications

• Experience with Deep Learning frameworks (TensorFlow, PyTorch).
• Familiarity with Cloud Platforms (AWS, Azure, or GCP) and their native data services.
• <strong>Databricks</strong> certifications, such as <strong>Databricks</strong> Certified Machine Learning Professional.

Benefits

• check_circle Employee assistance program
• check_circle Health insurance
• check_circle Paid time off