Data Scientist - Python

About The Role

Key Responsibilities

• Model Development : Design, prototype, and implement scalable ML models (e.g., classification, regression, clustering) and advanced statistical analysis.
• Data Preparation : Collect, clean, and preprocess large, complex, and unstructured datasets from various sources for model training.
• AI/GenAI Focus : Apply expertise in areas like Natural Language Processing (NLP), Computer Vision, or utilizing Large Language Models (LLMs) and techniques like RAG (Retrieval-Augmented Generation).
• Experimentation : Conduct A/B testing and statistical experiments to validate models and measure the business impact of deployed solutions.
• Deploymens : Collaborate with ML Engineers and DevOps to deploy models into production environments (MLOps).
• Communication : Present complex findings and recommendations clearly to both technical and non-technical stakeholders.
  

Essential Technical Skills

• Programming : Expert in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn).
• ML Frameworks : Strong experience with deep learning libraries like TensorFlow or PyTorch.
• Data/SQL : Proficiency in SQL for data extraction and manipulation; experience with big data technologies like Spark/PySpark.
• Cloud : Hands-on experience with cloud data/ML services (AWS Sagemaker, Azure ML, or Google AI Platform).
• Version Control : Familiarity with Git and MLOps tools (MLflow, DVC) for model and data versioning.
• Visualization : Proficient in data visualization tools (Matplotlib, Seaborn, Tableau/PowerBI)