Parkar Digital

Data Scientist

Parkar Digital

contract

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Mid-LevelSenior

Tech Stack

AzureCloudPySparkPythonPyTorchScikit-LearnTensorflow

About the role

  • 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.

Requirements

  • 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: Experience with MLOps practices and tools (CI/CD for ML, MLflow).
  • Good-to-have: Knowledge of vector databases and LLM deployment pipelines.
  • Good-to-have: Familiarity with prompt engineering and RAG techniques.
  • Good-to-have: Exposure to generative AI projects on cloud platforms.