Job Description

Job Title: Data Scientist

Location: Atlanta, GA (Remote)

About the Role:

We are seeking a highly motivated and skilled Data Scientist with strong expertise in data science fundamentals, machine learning (ML), and large language models (LLMs). The ideal candidate will have hands-on experience working with Databricks and Azure ecosystems, including PySpark for data processing and LLM tuning within Databricks. This role involves building and optimizing data science solutions that leverage cloud-based technologies to deliver business value.

Key Responsibilities:

• Design, develop, and deploy data science and ML solutions on Databricks (Azure environment).


• Work on end-to-end ML lifecycle, from data preparation and feature engineering to model training, evaluation, and deployment.


• Apply LLM fine-tuning and optimization techniques within Databricks for domain-specific use cases.


• Utilize PySpark for distributed data processing, cleaning, and transformation.


• Collaborate with data engineers, cloud architects, and business stakeholders to ensure seamless integration of ML models into production workflows.


• Conduct exploratory data analysis (EDA), statistical modeling, and hypothesis testing to extract insights from structured and unstructured data.


• Stay updated on the latest advancements in AI/ML, LLMs, and Databricks capabilities to bring innovative solutions.


• Document methodologies, experiments, and best practices for knowledge sharing.

Required Skills & Qualifications:

• Bachelor’s/Master’s degree in Computer Science, Data Science, Statistics, AI/ML, or related field.


• Proven experience as a Data Scientist with exposure to ML and NLP projects.


• Strong hands-on experience with Databricks on Azure (MLflow, Delta Lake, Databricks ML).


• Proficiency in PySpark for large-scale data processing.


• Experience in training, fine-tuning, and deploying LLMs within Databricks environment.


• Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face).


• Solid understanding of data science workflows: data wrangling, feature engineering, model development, and evaluation.


• Working knowledge of Azure cloud services (Azure Data Lake, Azure Synapse, Azure ML).


• Strong problem-solving, analytical thinking, and communication skills.

Good-to-Have Skills:

• Experience with MLOps practices and tools (CI/CD for ML, MLflow).


• Knowledge of vector databases and LLM deployment pipelines.


• Familiarity with prompt engineering and RAG (Retrieval-Augmented Generation) techniques.


• Exposure to generative AI projects on cloud platforms.

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